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Data Analysis With Open Source Tools

Data Analysis with Open Source Tools
by Philipp K. Janert

Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You’ll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.

Along the way, you’ll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you’ll learn how to think about the results you want to achieve — rather than rely on tools to think for you.

  • Use graphics to describe data with one, two, or dozens of variables
  • Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments
  • Mine data with computationally intensive methods such as simulation and clustering
  • Make your conclusions understandable through reports, dashboards, and other metrics programs
  • Understand financial calculations, including the time-value of money
  • Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations
  • Become familiar with different open source programming environments for data analysis

“Finally, a concise reference for understanding how to conquer piles of data.”–Austin King, Senior Web Developer, Mozilla

“An indispensable text for aspiring data scientists.”–Michael E. Driscoll, CEO/Founder, Dataspora


Bioinformatics Data Skills
by Vince Buffalo

Learn the data skills necessary for turning large sequencing datasets into reproducible and robust biological findings. With this practical guide, you’ll learn how to use freely available open source tools to extract meaning from large complex biological data sets.

At no other point in human history has our ability to understand life’s complexities been so dependent on our skills to work with and analyze data. This intermediate-level book teaches the general computational and data skills you need to analyze biological data. If you have experience with a scripting language like Python, you’re ready to get started.

  • Go from handling small problems with messy scripts to tackling large problems with clever methods and tools
  • Process bioinformatics data with powerful Unix pipelines and data tools
  • Learn how to use exploratory data analysis techniques in the R language
  • Use efficient methods to work with genomic range data and range operations
  • Work with common genomics data file formats like FASTA, FASTQ, SAM, and BAM
  • Manage your bioinformatics project with the Git version control system
  • Tackle tedious data processing tasks with with Bash scripts and Makefiles

Data Analysis with Open Source Tools
by Philipp Janert

Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You’ll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.

Along the way, you’ll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you’ll learn how to think about the results you want to achieve — rather than rely on tools to think for you.

  • Use graphics to describe data with one, two, or dozens of variables
  • Develop conceptual models using back-of-the-envelope calculations, as well as scaling and probability arguments
  • Mine data with computationally intensive methods such as simulation and clustering
  • Make your conclusions understandable through reports, dashboards, and other metrics programs
  • Understand financial calculations, including the time-value of money
  • Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations
  • Become familiar with different open source programming environments for data analysis

“Finally, a concise reference for understanding how to conquer piles of data.” –Austin King, Senior Web Developer, Mozilla

“An indispensable text for aspiring data scientists.” –Michael E. Driscoll, CEO/Founder, Dataspora


Open Source Geospatial Tools
by Daniel McInerney, Pieter Kempeneers

This book focuses on the use of open source software for geospatial analysis. It demonstrates the effectiveness of the command line interface for handling both vector, raster and 3D geospatial data. Appropriate open-source tools for data processing are clearly explained and discusses how they can be used to solve everyday tasks.

A series of fully worked case studies are presented including vector spatial analysis, remote sensing data analysis, landcover classification and LiDAR processing. A hands-on introduction to the application programming interface (API) of GDAL/OGR in Python/C++ is provided for readers who want to extend existing tools and/or develop their own software.


Data Analytics Using Open-Source Tools
by Jeffrey Strickland

This book is about Data Analytics. In that respect, it is like others. What distinguishes it from the rest is the variety of open-source tool applications. This book incorporates the use of R Studio, Python, SAS Studio (University Edition), and KNIME. This book is also about manipulating Big Data. Apache Hadoop on Hortonworks Sandbox is introduced and we manage, move, handle, and transform data using Apache Hive, Apache Spark, MapReduce and TEZ, with terminal shell commands and Ambari. We show you how to set up a virtual machine in Microsoft Azure. We then use the data in later chapters for modeling. We cover Descriptive Modeling and Predictive. The content includes Support Vector Machines, Decision Tree learning, Random Forests, Naive and Empirical Bayes, Gradient Boosting, Cluster Modeling, Generalized Linear Models, Logistic Regression, and Artificial Neural Networks. Every chapter includes completely worked examples using one or more open-source tools.”

Practical Data Analysis
by Hector Cuesta, Dr. Sampath Kumar

A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark

About This Book

  • Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data
  • Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images
  • A hands-on guide to understanding the nature of data and how to turn it into insight

Who This Book Is For

This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed.

What You Will Learn

  • Acquire, format, and visualize your data
  • Build an image-similarity search engine
  • Generate meaningful visualizations anyone can understand
  • Get started with analyzing social network graphs
  • Find out how to implement sentiment text analysis
  • Install data analysis tools such as Pandas, MongoDB, and Apache Spark
  • Get to grips with Apache Spark
  • Implement machine learning algorithms such as classification or forecasting

In Detail

Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service.

This book explains the basic data algorithms without the theoretical jargon, and you’ll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark.

Style and approach

This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.


Python for Data Analysis
by Wes McKinney

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.

Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.

  • Use the IPython shell and Jupyter notebook for exploratory computing
  • Learn basic and advanced features in NumPy (Numerical Python)
  • Get started with data analysis tools in the pandas library
  • Use flexible tools to load, clean, transform, merge, and reshape data
  • Create informative visualizations with matplotlib
  • Apply the pandas groupby facility to slice, dice, and summarize datasets
  • Analyze and manipulate regular and irregular time series data
  • Learn how to solve real-world data analysis problems with thorough, detailed examples

Open Source Software in Life Science Research
by Lee Harland, Mark Forster

The free/open source approach has grown from a minor activity to become a significant producer of robust, task-orientated software for a wide variety of situations and applications. To life science informatics groups, these systems present an appealing proposition – high quality software at a very attractive price. Open source software in life science research considers how industry and applied research groups have embraced these resources, discussing practical implementations that address real-world business problems.

The book is divided into four parts. Part one looks at laboratory data management and chemical informatics, covering software such as Bioclipse, OpenTox, ImageJ and KNIME. In part two, the focus turns to genomics and bioinformatics tools, with chapters examining GenomicsTools and EBI Atlas software, as well as the practicalities of setting up an ‘omics’ platform and managing large volumes of data. Chapters in part three examine information and knowledge management, covering a range of topics including software for web-based collaboration, open source search and visualisation technologies for scientific business applications, and specific software such as DesignTracker and Utopia Documents. Part four looks at semantic technologies such as Semantic MediaWiki, TripleMap and Chem2Bio2RDF, before part five examines clinical analytics, and validation and regulatory compliance of free/open source software. Finally, the book concludes by looking at future perspectives and the economics and free/open source software in industry.

  • Discusses a broad range of applications from a variety of sectors
  • Provides a unique perspective on work normally performed behind closed doors
  • Highlights the criteria used to compare and assess different approaches to solving problems

Data Simplification
by Jules J. Berman

Data Simplification: Taming Information With Open Source Tools addresses the simple fact that modern data is too big and complex to analyze in its native form. Data simplification is the process whereby large and complex data is rendered usable. Complex data must be simplified before it can be analyzed, but the process of data simplification is anything but simple, requiring a specialized set of skills and tools.

This book provides data scientists from every scientific discipline with the methods and tools to simplify their data for immediate analysis or long-term storage in a form that can be readily repurposed or integrated with other data.

Drawing upon years of practical experience, and using numerous examples and use cases, Jules Berman discusses the principles, methods, and tools that must be studied and mastered to achieve data simplification, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data, natural language processing and machine translation as a tool to simplify data, and data summarization and visualization and the role they play in making data useful for the end user.

  • Discusses data simplification principles, methods, and tools that must be studied and mastered
  • Provides open source tools, free utilities, and snippets of code that can be reused and repurposed to simplify data
  • Explains how to best utilize indexes to search, retrieve, and analyze textual data
  • Shows the data scientist how to apply ontologies, classifications, classes, properties, and instances to data using tried and true methods

Text Mining and Visualization
by Markus Hofmann, Andrew Chisholm

Text Mining and Visualization: Case Studies Using Open-Source Tools provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python.

The contributors—all highly experienced with text mining and open-source software—explain how text data are gathered and processed from a wide variety of sources, including books, server access logs, websites, social media sites, and message boards. Each chapter presents a case study that you can follow as part of a step-by-step, reproducible example. You can also easily apply and extend the techniques to other problems. All the examples are available on a supplementary website.

The book shows you how to exploit your text data, offering successful application examples and blueprints for you to tackle your text mining tasks and benefit from open and freely available tools. It gets you up to date on the latest and most powerful tools, the data mining process, and specific text mining activities.


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Think Complexity

Think Complexity
by Allen Downey

Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.

Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations.

In this updated second edition, you will:

  • Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform
  • Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines
  • Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata
  • Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism

Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.


Think Complexity
by Allen Downey

Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.

Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations.

In this updated second edition, you will:

  • Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform
  • Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines
  • Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata
  • Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism

Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.


Think Complexity
by Allen Downey

Expand your Python skills by working with data structures and algorithms in a refreshing context—through an eye-opening exploration of complexity science. Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of exercises, case studies, and easy-to-understand explanations.

You’ll work with graphs, algorithm analysis, scale-free networks, and cellular automata, using advanced features that make Python such a powerful language. Ideal as a text for courses on Python programming and algorithms, Think Complexity will also help self-learners gain valuable experience with topics and ideas they might not encounter otherwise.

  • Work with NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tables
  • Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines
  • Get starter code and solutions to help you re-implement and extend original experiments in complexity
  • Explore the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, and other topics
  • Examine case studies of complex systems submitted by students and readers

Think Bayes
by Allen Downey

If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you’ll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems.

Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start.

  • Use your existing programming skills to learn and understand Bayesian statistics
  • Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing
  • Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey
  • Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.

Think Stats
by Allen B. Downey

If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

You’ll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you’ll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.

  • Develop your understanding of probability and statistics by writing and testing code
  • Run experiments to test statistical behavior, such as generating samples from several distributions
  • Use simulations to understand concepts that are hard to grasp mathematically
  • Learn topics not usually covered in an introductory course, such as Bayesian estimation
  • Import data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics tools
  • Use statistical inference to answer questions about real-world data

Think Java
by Allen B. Downey, Chris Mayfield

Currently used at many colleges, universities, and high schools, this hands-on introduction to computer science is ideal for people with little or no programming experience. The goal of this concise book is not just to teach you Java, but to help you think like a computer scientist. You’ll learn how to program—a useful skill by itself—but you’ll also discover how to use programming as a means to an end.

Authors Allen Downey and Chris Mayfield start with the most basic concepts and gradually move into topics that are more complex, such as recursion and object-oriented programming. Each brief chapter covers the material for one week of a college course and includes exercises to help you practice what you’ve learned.

  • Learn one concept at a time: tackle complex topics in a series of small steps with examples
  • Understand how to formulate problems, think creatively about solutions, and write programs clearly and accurately
  • Determine which development techniques work best for you, and practice the important skill of debugging
  • Learn relationships among input and output, decisions and loops, classes and methods, strings and arrays
  • Work on exercises involving word games, graphics, puzzles, and playing cards

Think Simple
by Ken Segall

The secrets to Apple’s success and how to use them, from the Apple insider Ken Segall

In Think Simple, Apple insider and New York Times bestselling author Ken Segall gives you the tools to Apple’s success – and shows you how to use them. It’s all about simplicity.

Whether you’re in a multinational corporation or a lean startup, this guide will teach you how to crush complexity and focus on what matters; how to perform better, faster and more efficiently. Combining his insight from Apple with examples from companies across industries all over the world – including Ben & Jerry’s, Whole Foods, Intel and HyundaiCard – Segall provides a simple roadmap for any company to find success.


Embracing Complexity
by Jean G. Boulton, Peter M. Allen, Cliff Bowman

The book describes what it means to say the world is complex and explores what that means for managers, policy makers and individuals.

The first part of the book is about the theory and ideas of complexity. This is explained in a way that is thorough but not mathematical. It compares differing approaches, and also provides a historical perspective, showing how such thinking has been around since the beginning of civilisation. It emphasises the difference between a complexity worldview and the dominant mechanical worldview that underpins much of current management practice. It defines the complexity worldview as recognising the world is interconnected, shaped by history and the particularities of context. The comparison of the differing approaches to modelling complexity is unique in its depth and accessibility.

The second part of the book uses this lens of complexity to explore issues in the fields of management, strategy, economics, and international development. It also explores how to facilitate others to recognise the implications of adopting a complex rather than a mechanical worldview and suggests methods of research to explore systemic, path-dependent emergent aspects of situations.

The authors of this book span both science and management, academia and practice, thus the explanations of science are authoritative and yet the examples of changing how you live and work in the world are real and accessible. The aim of the book is to bring alive what complexity is all about and to illustrate the importance of loosening the grip of a modernist worldview with its hope for prediction, certainty and control.


Systems Thinking
by Jamshid Gharajedaghi

Systems Thinking, Third Edition combines systems theory and interactive design to provide an operational methodology for defining problems and designing solutions in an environment increasingly characterized by chaos and complexity. This new edition has been updated to include all new chapters on self-organizing systems as well as holistic, operational, and design thinking.

The book covers recent crises in financial systems and job markets, the housing bubble, and environment, assessing their impact on systems thinking. A companion website is available at interactdesign.com.

This volume is ideal for senior executives as well as for chief information/operating officers and other executives charged with systems management and process improvement. It may also be a helpful resource for IT/MBA students and academics.

  • Four NEW chapters on self-organizing systems, holistic thinking, operational thinking, and design thinking
  • Covers the recent crises in financial systems and job markets globally, the housing bubble, and the environment, assessing their impact on systems thinking
  • Companion website to accompany the book is available at interactdesign.com

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Cathedral And The Bazaar

The Cathedral & the Bazaar
by Eric S. Raymond

Open source provides the competitive advantage in the Internet Age. According to the August Forrester Report, 56 percent of IT managers interviewed at Global 2,500 companies are already using some type of open source software in their infrastructure and another 6 percent will install it in the next two years. This revolutionary model for collaborative software development is being embraced and studied by many of the biggest players in the high-tech industry, from Sun Microsystems to IBM to Intel.The Cathedral & the Bazaar is a must for anyone who cares about the future of the computer industry or the dynamics of the information economy. Already, billions of dollars have been made and lost based on the ideas in this book. Its conclusions will be studied, debated, and implemented for years to come. According to Bob Young, “This is Eric Raymond’s great contribution to the success of the open source revolution, to the adoption of Linux-based operating systems, and to the success of open source users and the companies that supply them.”The interest in open source software development has grown enormously in the past year. This revised and expanded paperback edition includes new material on open source developments in 1999 and 2000. Raymond’s clear and effective writing style accurately describing the benefits of open source software has been key to its success. With major vendors creating acceptance for open source within companies, independent vendors will become the open source story in 2001.


The Cathedral & the Bazaar
by Eric S. Raymond

Open source provides the competitive advantage in the Internet Age. According to the August Forrester Report, 56 percent of IT managers interviewed at Global 2,500 companies are already using some type of open source software in their infrastructure and another 6 percent will install it in the next two years. This revolutionary model for collaborative software development is being embraced and studied by many of the biggest players in the high-tech industry, from Sun Microsystems to IBM to Intel.

The Cathedral & the Bazaar is a must for anyone who cares about the future of the computer industry or the dynamics of the information economy. Already, billions of dollars have been made and lost based on the ideas in this book. Its conclusions will be studied, debated, and implemented for years to come. According to Bob Young, “This is Eric Raymond’s great contribution to the success of the open source revolution, to the adoption of Linux-based operating systems, and to the success of open source users and the companies that supply them.”

The interest in open source software development has grown enormously in the past year. This revised and expanded paperback edition includes new material on open source developments in 1999 and 2000. Raymond’s clear and effective writing style accurately describing the benefits of open source software has been key to its success. With major vendors creating acceptance for open source within companies, independent vendors will become the open source story in 2001.


The Cathedral and the Bazaar
by Eric S. Raymond

The Cathedral and the Bazaar is an essay by Eric S. Raymond on software engineering methods, based on his observations of the Linux kernel development process and his experiences managing an open source project, fetchmail. It was first presented by the author at the Linux Kongress on May 27, 1997 in Wurzburg and was published as part of a book of the same name in 1999. Eric Steven Raymond (born December 4, 1957), often referred to as ESR, is a computer programmer, author and open source software advocate. His name became known within the hacker culture when he became the maintainer of the “Jargon File.” After the 1997 publication of “The Cathedral and the Bazaar,” Raymond became, for a number of years, an unofficial spokesman of the open source movement.”

The cathedral and the bazaar
by Eric S. Raymond

Open source provides the competitive advantage in the Internet Age. According to the August Forrester Report, 56 percent of IT managers interviewed at Global 2,500 companies are already using some type of open source software in their infrastructure and another 6 percent will install it in the next two years. This revolutionary model for collaborative software development is being embraced and studied by many of the biggest players in the high-tech industry, from Sun Microsystems to IBM to Intel. The Cathedral & the Bazaaris a must for anyone who cares about the future of the computer industry or the dynamics of the information economy. Already, billions of dollars have been made and lost based on the ideas in this book. Its conclusions will be studied, debated, and implemented for years to come. According to Bob Young, “This is Eric Raymond’s great contribution to the success of the open source revolution, to the adoption of Linux-based operating systems, and to the success of open source users and the companies that supply them.” The interest in open source software development has grown enormously in the past year. This revised and expanded hardback edition includes new material on open source developments in 1999 and 2000. Raymond’s clear and effective writing style accurately describing the benefits of open source software has been key to its success. With major vendors creating acceptance for open source within companies, independent vendors will become the open source story in 2001.

Producing Open Source Software
by Karl Fogel

The corporate market is now embracing free, “open source” software like never before, as evidenced by the recent success of the technologies underlying LAMP (Linux, Apache, MySQL, and PHP). Each is the result of a publicly collaborative process among numerous developers who volunteer their time and energy to create better software.

The truth is, however, that the overwhelming majority of free software projects fail. To help you beat the odds, O’Reilly has put together Producing Open Source Software, a guide that recommends tried and true steps to help free software developers work together toward a common goal. Not just for developers who are considering starting their own free software project, this book will also help those who want to participate in the process at any level.

The book tackles this very complex topic by distilling it down into easily understandable parts. Starting with the basics of project management, it details specific tools used in free software projects, including version control, IRC, bug tracking, and Wikis. Author Karl Fogel, known for his work on CVS and Subversion, offers practical advice on how to set up and use a range of tools in combination with open mailing lists and archives. He also provides several chapters on the essentials of recruiting and motivating developers, as well as how to gain much-needed publicity for your project.

While managing a team of enthusiastic developers — most of whom you’ve never even met — can be challenging, it can also be fun. Producing Open Source Software takes this into account, too, as it speaks of the sheer pleasure to be had from working with a motivated team of free software developers.


Rebel Code
by Glyn Moody

The open source saga has many fascinating chapters. It is partly the story of Linus Torvalds, the master hacker who would become chief architect of the Linux operating system. It is also the story of thousands of devoted programmers around the world who spontaneously worked in tandem to complete the race to shape Linux into the ultimate killer app. Rebel Code traces the remarkable roots of this unplanned revolution. It echoes the twists and turns of Linux’s improbable development, as it grew through an almost biological process of accretion and finally took its place at the heart of a jigsaw puzzle that would become the centerpiece of open source. With unprecedented access to the principal players, Moody has written a powerful tale of individual innovation versus big business. Rebel Code provides a from-the-trenches perspective and looks ahead to how open source is challenging long-held conceptions of technology, commerce, and culture.

Biobazaar
by Janet Hope

Can the open source approach do for biotechnology what it has done for information technology? Hope’s book is the first sustained and systematic inquiry into the application of open source principles to the life sciences. Traversing disciplinary boundaries, she presents a careful analysis of intellectual property-related challenges confronting the biotechnology industry and then paints a detailed picture of “open source biotechnology” as a possible solution.

Open Source Systems
by Ernesto Damiani, Brian Fitzgerald, Walt Scacchi, Marco Scotto

Early research studies on open source software development often betrayed a mild surprise that loosely coordinated networks of volunteers could manage the design and implementation of highly comple software products. In the past few years, a wider research community has become increasingly aware of the tremendous contribution that open source development is making to the software industry, business and society in general. Software engineering researchers are exploring OSS specifically with respect to development tools and methodologies, while organizational scientists and economists are keen on understanding how open sources have brought large communities of people to help each other effectively.

This book is an important step in the direction of a fuller understanding of the OSS phenomenon. It collects the proceedings of the Second International Conference on Open Software held in Como, Italy, from June 8th to June 10th, 2006. OSS 2006 was the foundation conference of the IFIP TC 2 WG 2.13 on Open Source Software, and attracted many researchers from all over the world interested in how OSS is produced, in its huge innovation potential in many different application fields and in OSS innovative business models. The 20 full papers of this volume were selected via a rigourous refereeing process among more than 100 submissions; 12 additional submissions, in view of their interest, were selected for publication in a more concise form.


Understanding Open Source and Free Software Licensing
by Andrew M. St. Laurent

If you’ve held back from developing open source or free software projects because you don’t understand the implications of the various licenses, you’re not alone. Many developers believe in releasing their software freely, but have hesitated to do so because they’re concerned about losing control over their software. Licensing issues are complicated, and both the facts and fallacies you hear word-of-mouth can add to the confusion.

Understanding Open Source and Free Software Licensing helps you make sense of the different options available to you. This concise guide focuses on annotated licenses, offering an in-depth explanation of how they compare and interoperate, and how license choices affect project possibilities. Written in clear language that you don’t have to be a lawyer to understand, the book answers such questions as: What rights am I giving up? How will my use of OS/FS licensing affect future users or future developers? Does a particular use of this software–such as combining it with proprietary software–leave me vulnerable to lawsuits?

Following a quick look at copyright law, contracts, and the definition of “open source,” the book tackles the spectrum of licensing, including:

  • The MIT (or X), BSD, Apache and Academic Free licenses
  • The GPL, LGPL, and Mozilla licenses
  • The QT, Artistic, and Creative Commons licenses
  • Classic Proprietary licenses
  • Sun Community Source license and Microsoft Shared Source project

The book wraps up with a look at the legal effects–both positive and negative–of open source/free software licensing.

Licensing is a major part of what open source and free software are all about, but it’s still one of the most complicated areas of law. Even the very simple licenses are tricky. Understanding Open Source and Free Software Licensing bridges the gap between the open source vision and the practical implications of its legal underpinnings. If open source and free software licenses interest you, this book will help you understand them. If you’re an open source/free software developer, this book is an absolute necessity.


Free Software, Free Society
by Richard Stallman

Essay Collection covering the point where software, law and social justice meet.

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Pointers In C

Pointers on C
by Kenneth A. Reek

This pathbreaking book provides for the first time an overview of the genesis, growth, gains, and dilemmas of women’s movements worldwide. Unlike most of the literature, which focuses on the industrialized Western world, this volume devotes greater attention to the postcolonial states of Asia, Africa, and Latin America. The book challenges the assumptions that feminism can transcend national differences and, conversely, that women’s movements are shaped and circumscribed by national levels of development. All the authors reject the notion, proposed by its detractors and champions alike, that feminism is of middle-class origins and Western inspiration. Instead they seek to locate women’s movements within the terrain from which they emerge.Virtually all the authors are from the countries or communities about which they write; the few exceptions are women who have spent lengthy periods studying and living in the region. Most are scholars, often in women’s studies, and many are closely associated with the movements they describe. Thus, these writers share a commitment to the substantive concerns as well as the collective processes of women’s movements. As a key book for the Fourth World Conference on Women in Beijing, this volume will be essential reading for anyone interested in the global scope and implications of feminism.

Understanding and Using C Pointers
by Richard Reese

Improve your programming through a solid understanding of C pointers and memory management. With this practical book, you’ll learn how pointers provide the mechanism to dynamically manipulate memory, enhance support for data structures, and enable access to hardware. Author Richard Reese shows you how to use pointers with arrays, strings, structures, and functions, using memory models throughout the book.

Difficult to master, pointers provide C with much flexibility and power—yet few resources are dedicated to this data type. This comprehensive book has the information you need, whether you’re a beginner or an experienced C or C++ programmer or developer.

  • Get an introduction to pointers, including the declaration of different pointer types
  • Learn about dynamic memory allocation, de-allocation, and alternative memory management techniques
  • Use techniques for passing or returning data to and from functions
  • Understand the fundamental aspects of arrays as they relate to pointers
  • Explore the basics of strings and how pointers are used to support them
  • Examine why pointers can be the source of security problems, such as buffer overflow
  • Learn several pointer techniques, such as the use of opaque pointers, bounded pointers and, the restrict keyword

Pointers in C
by Hrishikesh Dewan, Naveen Toppo

Pointers in C provides a resource for professionals and advanced students needing in-depth but hands-on coverage of pointer basics and advanced features. The goal is to help programmers in wielding the full potential of pointers.

In spite of its vast usage, understanding and proper usage of pointers remains a significant problem. This book’s aim is to first introduce the basic building blocks such as elaborate details about memory, the compilation process (parsing/preprocessing/assembler/object code generation), the runtime memory organization of an executable and virtual memory. These basic building blocks will help both beginners and advanced readers to grasp the notion of pointers very easily and clearly. The book is enriched with several illustrations, pictorial examples, and code from different contexts (Device driver code snippets, algorithm, and data structures code where pointers are used).

Pointers in C contains several quick tips which will be useful for programmers for not just learning the pointer concept but also while using other features of the C language. Chapters in the book are intuitive, and there is a strict logical flow among them and each chapter forms a basis for the next chapter. This book contains every small aspect of pointer features in the C language in their entirety.


Mastering C Pointers
by Robert J. Traister

Mastering C Pointers: Tools for Programming Power focuses on the pointer operations of the C programming language, explaining exactly what pointers are and how to master them through easy-to-understand phrasing and by presenting many simple program examples. The functions of pointers with respect to memory access and memory allocation are also discussed.

Comprised of 10 chapters, this book begins with the author’s personal reflection on his first encounters with the C programming language and its pointers. The next two chapters presents steps to learning pointers, with emphasis on the essential processes that occur (invisibly and internally) when declaring standard numeric variables in C language and how to deal with C language character arrays and C strings. The reader is then introduced to string pointers and declared pointers of numeric types; the use of C language pointers and the memory allocation functions; and C language functions. The book also explores some of the other “entities” that pointers are used to access, including structures and unions, before concluding with an examination of the source code format of C language.

This monograph is intended for both beginning and experienced C language programmers.


Pointers in The C Programming Language
by Ninnat Aupala

One of the most difficult and important thing in C is pointers. However, the concept of pointers often is not explained in detail in most C textbooks. This book is designed to provide an understanding about pointers in depth. Try this book, If you have a trouble with pointers


C++ Pointers and Dynamic Memory Management
by Michael C. Daconta

Using techniques developed in the classroom at America Online’s Programmer’s University, Michael Daconta deftly pilots programmers through the intricacies of the two most difficult aspects of C++ programming: pointers and dynamic memory management. Written by a programmer for programmers, this no-nonsense, nuts-and-bolts guide shows you how to fully exploit advanced C++ programming features, such as creating class-specific allocators, understanding references versus pointers, manipulating multidimensional arrays with pointers, and how pointers and dynamic memory are the core of object-oriented constructs like inheritance, name-mangling, and virtual functions.

Covers all aspects of pointers including: pointer pointers, function pointers, and even class member pointers

  • Over 350 source code functions—code on every topic
  • OOP constructs dissected and implemented in C
  • Interviews with leading C++ experts
  • Valuable money-saving coupons on developer products
  • Free source code disk
  • Disk includes: Reusable code libraries—over 350 source code functions you can use to protect and enhance your applications
  • Memory debugger

Read C++ Pointers and Dynamic Memory Management and learn how to combine the elegance of object-oriented programming with the power of pointers and dynamic memory!


Head First C
by David Griffiths, Dawn Griffiths

Ever wished you could learn C from a book? Head First C provides a complete learning experience for C and structured imperative programming. With a unique method that goes beyond syntax and how-to manuals, this guide not only teaches you the language, it helps you understand how to be a great programmer. You’ll learn key areas such as language basics, pointers and pointer arithmetic, and dynamic memory management. Advanced topics include multi-threading and network programming—topics typically covered on a college-level course.

This book also features labs: in-depth projects intended to stretch your abilities, test your new skills, and build confidence. Head First C mimics the style of college-level C courses, making it ideal as an accessible textbook for students.

We think your time is too valuable to waste struggling with new concepts. Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First C uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.


Introductory C
by Richard Petersen

C differs from most programming languages in its use of pointers, functions, and files. For those learning C, pointers are the greatest source of confusion. The primary aim of this book is to provide working models of how pointers are used in C. Pointers are used differently with every programming feature and Introductory C, Second Edition, emphasizes how each new programming feature relates to the use of pointers to provide a thorough understanding of the applications throughout C. Introductory C, Second Edition, offers a detailed explanation of Arrays in C over several chapters, describing strings, arrays of structures, multi-dimensional arrays, and arrays of pointers. The underlying implementation of arrays using pointersis described in detail with such features as pointers to elements, pointers to arrays, and pointers to pointers. File access methods using text and binary files are also given thorough coverage: character, line, formatted text files, and record binary files are described, and the implementation of file access functions is described in detail. This book will be of great use to programmers at all levels. Key Features * Includes three new chapters introducing C++, including coverage of classes, objects, constructors, overloading, inheritance, and derivation * Contains a floppy disk with source code from examples in the book

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Radiowaril Bhashane & Shrutika

Data Scientists at Work
by Sebastian Gutierrez

Data Scientists at Work is a collection of interviews with sixteen of the world’s most influential and innovative data scientists from across the spectrum of this hot new profession. “Data scientist is the sexiest job in the 21st century,” according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report.

Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (André Karpištšenko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google’s Director of Research, Peter Norvig.

Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees’ earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients.


Machine Learning for Hackers
by Drew Conway, John Myles White

If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.

Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.

  • Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text
  • Use linear regression to predict the number of page views for the top 1,000 websites
  • Learn optimization techniques by attempting to break a simple letter cipher
  • Compare and contrast U.S. Senators statistically, based on their voting records
  • Build a “whom to follow” recommendation system from Twitter data

Journal of a Novel
by John Steinbeck

Each working day from January 29 to November 1, 1951, John Steinbeck warmed up to the work of writing East of Eden with a letter to the late Pascal Covici, his friend and editor at The Viking Press. It was his way, he said, of “getting my mental arm in shape to pitch a good game.”

 

Steinbeck’s letters were written on the left-hand pages of a notebook in which the facing pages would be filled with the test of East of Eden. They touched on many subjects—story arguments, trial flights of workmanship, concern for his sons.

Part autobiography, part writer’s workshop, these letters offer an illuminating perspective on Steinbeck’s creative process, and a fascinating glimpse of Steinbeck, the private man.


The New Machiavelli
by H. G. Wells

“The New Machiavelli” is a novel written by H. G. Wells, first published in 1911. The plot was well-known to have been based on Wells’ affair with Amber Reeves and a satire of Beatrice and Sidney web; and, as such, was constituted a veritable literary scandal at the time. An interesting and entertaining story of life and loves, “The New Machiavelli” will not disappoint fans of Wells work and deserves a place on every bookshelf. Contents include: “The Making Of A Man”, “Concerning A Book That Was Never Written”, “Bromstead And My Father”, “Margaret In London”, et cetera. Herbert George Wells (1866 – 1946) was a prolific English writer who wrote in a variety of genres, including the novel, politics, history, and social commentary. Today, he is perhaps best remembered for his contributions to the science fiction genre thanks to such novels as “The Time Machine” (1895), “The Invisible Man” (1897), and “The War of the Worlds” (1898). Many vintage books such as this are becoming increasingly scarce and expensive. We are republishing this book now in an affordable, modern, high-quality edition complete with a specially commissioned new biography of the author.

In a Free State
by V. S. Naipaul

No writer has rendered our boundariless, post-colonial world more acutely or prophetically than V. S. Naipaul, or given its upheavals such a hauntingly human face. A perfect case in point is this riveting novel, a masterful and stylishly rendered narrative of emigration, dislocation, and dread, accompanied by four supporting narratives.

In the beginning it is just a car trip through Africa. Two English people—Bobby, a civil servant with a guilty appetite for African boys, and Linda, a supercilious “compound wife”—are driving back to their enclave after a stay in the capital. But in between lies the landscape of an unnamed country whose squalor and ethnic bloodletting suggest Idi Amin’s Uganda. And the farther Naipaul’s protagonists travel into it, the more they find themselves crossing the line that separates privileged outsiders from horrified victims. Alongside this Conradian tour de force are four incisive portraits of men seeking liberation far from home. By turns funny and terrifying, sorrowful and unsparing, In A Free State is Naipaul at his best.


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Let Us C

Let Us C
by Yashavant P. Kanetkar

Considered to be one of the best-selling programming books ever written, the eighth edition has now been edited, revised, and updated. A CD-ROM with demos, code, compiler, executables, and MATLAB examples has been added to the book. Simplicity and an easy narration style are the hallmarks of the book, which have made its previous seven editions immensely successful. Today’s C programmer (still the language of choice in science, engineering, game programming and for handheld devices) has to master the complexities of the language and contend with its usage in environments like Windows, Linux, and for the Internet. Let Us C, Eighth Edition covers these three aspects of C programming and doesn?t assume any programming background. It begins with the basics and steadily builds the pace, so the reader finds it easy to handle more complicated topics later. This popular author has crafted hundreds of excellent programming examples and exercises for every aspect of C programming.

LET US C SOLUTIONS -15TH EDITION
by Yashavant kanetkar

Description: Best way to learn any programming language is to create good programs in it. C is not exception to this rule. Once you decide to write any program you would find that there are always at least two ways to write it. So you need to find out whether you have chosen the best way to implement your program. That’s where you would find this book useful. It contains solutions to all the exercises present in Let Us C 15th Edition. If you learn the language elements from Let Us C, write programs for the problems given in the exercises and then cross check your answers with the solutions given in this book you would be well on your way to become a skilled C programmer. I am sure you would appreciate this learning path like the millions of students and professionals have in the past decade.Table Of Contents:IntroductionChapter 0 : Before We beginChapter 1 : Getting StartedChapter 2 : C InstructionsChapter 3 : Decision Control InstructionChapter 4 : More Complex Decision MakingChapter 5 : Loop control InstructionChapter 6 : More Complex RepetitionsChapter 7 : Case Control InstructionChapter 8 : FunctionsChapter 9 : PointersChapter 10 : RecursionChapter 11 : Data Types RevisitedChapter 12 : The C PreprocessorChapter 13 : ArraysChapter 14 : Multidimensional ArraysChapter 15 : StringsChapter 16 : Handling Multiple StringsChapter 17 : StructuresChapter 18 : Console Input/ OutputChapter 19 : File Input/outputChapter 20 : More Issues in Input/OutputChapter 21 : Operations on BitsChapter 22 : Miscellaneous featuresChapter 23 : C Under Linux

Let us C 16th Edition
by kanetkar Yashavant

Let Us C has been part of learning and teaching material in mostO Over three million copies sold worldwide.Authentic Guide to C Programming LanguageBasic / Intermediate/Advanced C Programming, C Under Unix and GNOME Programming Using GTK Library DESCRIPTION”e; Simplicity”e; -that has been the hallmark of this book in not only its previous fourteen English editions, but also in the Hindi, Gujarati, Japanese, Korean, Chinese and US editions. This book doesn’t assume any programming background. It begins with the basics and steadily builds the pace so that the reader finds it easy to handle advanced topics towards the end of the book. Easter chapter contains: Lucid explanation of the conceptWell though-out, fully working programming examplesEnd-of-chapter exercises that would help you practise the skills learned in the chapterHand-crafted “e;KanNotes”e; that would help you remember and revise the concepts covered in each chapterEngineering and Science Institutes round the country for years now.From last year or so, I received several suggestions that its size bepruned a bit, as many learners who learn C language in their Engineeringor Science curriculum have some familiarity with it. I am happy to fulfillthis request. I hope the readers would appreciate the lean look of thecurrent edition.In one of the previous edition I had realigned the chapters in such amanner that if a C programming course is taught using Let Us C, it can befinished in 22 lectures of one hour each, with one chapter’s contentsdevoted to one lecture. I am happy that many readers liked this idea andreported that this has made their learning path trouble-free. A morerational reorganization of end-of-chapter exercises in the book has alsobeen well-received. Riding on that feedback I had introduced one morefeature in the fifteenth edition-KanNotes. These are hand-craftednotes on C programming. From the reader’s emails I gather that theyhave turned out to be very useful to help revise their concepts on theday before the examination, viva-voce or interview.Many readers also told me that they have immensely benefitted fromthe inclusion of the chapter on Interview FAQs. I have improved thischapter further. The rationale behind this chapter is simple-ultimatelyall the readers of Let Us C sooner or later end up in an interview roomwhere they are required to take questions on C programming. I nowhave a proof that this chapter has helped to make that journey smoothand fruitful.All the programs present in the book (and some more) are available insource code form at www.kicit.com/books/letusc/sourcecode. You arefree to download them, improve them, change them, do whatever withthem. If you wish to get solutions for the Exercises in the book they areavailable in another book titled ‘Let Us C Solutions’. If you want somemore problems for practice they are available in the book titled ‘Let Us CWorkbook’. As usual, new editions of these t

LET US JAVA-3rd EDITION
by Yashavant kanetkar

Description:Java Language is very popularly used for creating applications for PC, Laptop, Tablet, Web and Mobile world Learning a language that can work on so many different platforms can be a challenge. This is where you would find this book immediately useful. It follows simple and easy narration style. It doesn’t assume any programming background. It begins with the basics and steadily builds the pace so that the reader finds it easy to handle complex topics towards the end. Each chapter has been designed to create a deep and lasting impression on reader’s mind. Object Oriented Programming has been covered in detail to give a strong foundation for Java Programming. Well thought out and fully working example programs and carefully crafted exercises of this book, cover every aspect of Java programming. Well through out and finally working examples, and carefully crafted exercises of this book, covers every aspect of Java Programming. Some of the highlighting features of this book are: A* Data types & Control InstructionsA* Object Oriented ProgrammingA* Classes & ObjectsA* Arrays & StringsA* Inheritance & PolymorphismA* InterfacesA* PackagesA* Exception HandlingA* Effective IOA* Multithreading & SynchronizationA* GenericsA* Collection ClassesA* GUI Using SwingA* Database Connectivity UsingJDBc Table Of Contents:-An Overview of Java-Getting Started-More about Data Types-Decision Control Instruction-Loop Control Instruction-Case Control Instruction-Functions-Advanced Features of Functions-Introduction to OOP-Classes and Objects-Arrays-Strings and Enums-Inheritance-Polymorphism-Exception Handling-Effective Input/ Output-Multithreading in Java-Generics-Collection Classes-User Interfaces-JDBC-Index

Programming In Ansi C
by E Balagurusamy

This book presents a detailed exposition of C in an extremely simple style. The various features of the language have been systematically discussed. The entire text has been reviewed and revised incorporating the feedback from the readers. Each chapter has been expanded to include a variety of solved examples and practice problems.

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Computer Vision

Computer Vision
by Richard Szeliski

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art?

Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.

More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques

Topics and features:

  • Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses
  • Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects
  • Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory
  • Suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book
  • Supplies supplementary course material for students at the associated website, http://szeliski.org/Book/

Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.


Computer Vision
by Richard Szeliski

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art?

Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.

More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques

Topics and features:

  • Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses
  • Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects
  • Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory
  • Suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book
  • Supplies supplementary course material for students at the associated website, http://szeliski.org/Book/

Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.


Computer Vision
by Anup Basu, Xiaobo Li

This book contains a selection of papers which were presented at the Vision Interface ’92 Conference. It also includes several invited articles from prominent researchers in the field, suggesting future directions in Computer Vision.

Deep Learning for Computer Vision
by Rajalingappaa Shanmugamani

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks

Key Features

  • Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision
  • Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more
  • Includes tips on optimizing and improving the performance of your models under various constraints

Book Description

Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning.

In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.

What you will learn

  • Set up an environment for deep learning with Python, TensorFlow, and Keras
  • Define and train a model for image and video classification
  • Use features from a pre-trained Convolutional Neural Network model for image retrieval
  • Understand and implement object detection using the real-world Pedestrian Detection scenario
  • Learn about various problems in image captioning and how to overcome them by training images and text together
  • Implement similarity matching and train a model for face recognition
  • Understand the concept of generative models and use them for image generation
  • Deploy your deep learning models and optimize them for high performance

Who this book is for

This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.


A Guide to Convolutional Neural Networks for Computer Vision
by Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun

Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision.

This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation.

This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.


Computer Vision, Models, and Inspection
by A. Dave Marshall, Ralph R. Martin

The main focus of this book is on the uses of computer vision for inspection and model based matching. It also provides a short, self contained introductory course on computer vision. The authors describe various state-of-the-art approaches to probems and then set forth their proposed approach to matching and inspection. They deal primarily with 3-D vision but also discuss 2-D vision strategies when relevant.The book is suitable for researchers, final year undergraduates and graduate students. Useful review questions at the end of each chapter allow this book to be used for self-study.

Handbook of Machine and Computer Vision
by Alexander Hornberg

The second edition of this accepted reference work has been updated to reflect the rapid developments in the field and now covers both 2D and 3D imaging.
Written by expert practitioners from leading companies operating in machine vision, this one-stop handbook guides readers through all aspects of image acquisition and image processing, including optics, electronics and software. The authors approach the subject in terms of industrial applications, elucidating such topics as illumination and camera calibration. Initial chapters concentrate on the latest hardware aspects, ranging from lenses and camera systems to camera-computer interfaces, with the software necessary discussed to an equal depth in later sections. These include digital image basics as well as image analysis and image processing. The book concludes with extended coverage of industrial applications in optics and electronics, backed by case studies and design strategies for the conception of complete machine vision systems. As a result, readers are not only able to understand the latest systems, but also to plan and evaluate this technology.
With more than 500 images and tables to illustrate relevant principles and steps.

Computer Vision
by Pedram Azad, Tilo Gockel, Rüdiger Dillmann

Computer vision is probably the most exciting branch of image processing, and the number of applications in robotics, automation technology and quality control is constantly increasing. Unfortunately entering this research area is, as yet, not simple. Those who are interested must first go through a lot of books, publications and software libraries. With this book, however, the first step is easy. The theoretically founded content is understandable and is supplemented by many practical examples. Source code is provided with the specially developed platform-independent open source library IVT in the programming language C/C++. The use of the IVT is not necessary, but it does make for a much easier entry and allows first developments to be quickly produced.

Advances in Computer Vision and Information Technology
by K. V. Kale

The latest trends in information technology represent a new intellectual paradigm for scientific exploration and the visualization of scientific phenomena. This title covers the emerging technologies in the field. Academics, engineers, industrialists, scientists and researchers engaged in teaching, and research and development of computer science and information technology will find the book useful for their academic and research work. This text includes 225 articles covering the following topics:

  • Advance Networking and Security/Wireless Networking/Cyber Laws.
  • Advance Software Computing.
  • Artificial Intelligence/Natural Language Processing/Neural Networks.
  • Bioinformatics/Biometrics.
  • Data Mining/E-Commerce/E-Learning.
  • Image Processing, Content Based Image Retrieval, Medical and Bio-Medical Imaging, Wavelets.
  • Information Processing/Audio and Text Processing/Cryptology, Steganography and Digital Watermarking.
  • Pattern Recognition/Machine Vision/Image Motion, Video Processing.
  • Signal Processing and Communication/Remote Sensing.
  • Speech Processing & Recognition, Human Computer Interaction.
  • Information and Communication Technology.

Readings in Computer Vision
by Martin A. Fischler, Oscar Firschein

The field of computer vision combines techniques from physics, mathematics, psychology, artificial intelligence, and computer science to examine how machines might construct meaningful descriptions of their surrounding environment. The editors of this volume, prominent researchers and leaders of the SRI International AI Center Perception Group, have selected sixty papers, most published since 1980, with the viewpoint that computer vision is concerned with solving seven basic problems:

  • Reconstructing 3D scenes from 2D images
  • Decomposing images into their component parts
  • Recognizing and assigning labels to scene objects
  • Deducing and describing relations among scene objects
  • Determining the nature of computer architectures that can support the visual function
  • Representing abstractions in the world of computer memory
  • Matching stored descriptions to image representation

Each chapter of this volume addresses one of these problems through an introductory discussion, which identifies major ideas and summarizes approaches, and through reprints of key research papers. Two appendices on crucial assumptions in image interpretation and on parallel architectures for vision applications, a glossary of technical terms, and a comprehensive bibliography and index complete the volume.


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Data Structures Using C & C++

Open Data Structures
by Pat Morin

Offered as an introduction to the field of data structures and algorithms, Open Data Structures covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs. Focusing on a mathematically rigorous approach that is fast, practical, and efficient, Morin clearly and briskly presents instruction along with source code.


Data Structures Using C++
by D. S. Malik

Now in its second edition, D.S. Malik brings his proven approach to C++ programming to the CS2 course. Clearly written with the student in mind, this text focuses on Data Structures and includes advanced topics in C++ such as Linked Lists and the Standard Template Library (STL). The text features abundant visual diagrams, examples, and extended Programming Examples, all of which serve to illuminate difficult concepts. Complete programming code and clear display of syntax, explanation, and example are used throughout the text, and each chapter concludes with a robust exercise set.
Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Data Structures Using C
by Samir Kumar Bandyopadhyay

Data Structures Using C brings together a first course on data structures and the complete programming techniques, enabling students and professionals implement abstract structures and structure their ideas to suit different needs. This book elaborates the standard data structures using C as the basic programming tool. It is designed for a one semester course on Data Structures.

Think Data Structures
by Allen Downey

If you’re a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering—data structures and algorithms—in a way that’s clearer, more concise, and more engaging than other materials.

By emphasizing practical knowledge and skills over theory, author Allen Downey shows you how to use data structures to implement efficient algorithms, and then analyze and measure their performance. You’ll explore the important classes in the Java collections framework (JCF), how they’re implemented, and how they’re expected to perform. Each chapter presents hands-on exercises supported by test code online.

  • Use data structures such as lists and maps, and understand how they work
  • Build an application that reads Wikipedia pages, parses the contents, and navigates the resulting data tree
  • Analyze code to predict how fast it will run and how much memory it will require
  • Write classes that implement the Map interface, using a hash table and binary search tree
  • Build a simple web search engine with a crawler, an indexer that stores web page contents, and a retriever that returns user query results

Other books by Allen Downey include Think Java, Think Python, Think Stats, and Think Bayes.


Algorithms and Data Structures – Applications to Graphics and Geometry
by Textbook Equity

This is a college-level introductory textbook of algorithms and data structures with application to graphics and geometry. This textbook, released under a Creative Commons Share Alike (CC BY SA) license, is presented in its original format with the academic content unchanged. It was authored by Jurg Nievergelt (ETH Zurich) and Klaus Hinrichs (Institut fur Informatik) and provided by the University of Georgia’s Global Textbook Project. Textbookequity.org/algorithms-and-data-structures/ Photo Credit: Renato Keshet (GFDL) commons.wikimedia.org Contents Part I: Programming environments for motion, graphics, and geometry Part II: Programming concepts: beyond notation Part IV: Complexity of problems and algorithms Part V: Data structures Textbook Equity Edition http: //textbookequity.org/algorithms-and-data-structures

Advanced Data Structures
by A.A.Puntambekar

Different strategies for problem solving, Need for OOP, Overview of OOP principles-encapsulation, Inheritance, Polymorphism. C++ class overview-class definition, Objects, Class members, Access control, Class scope, Constructors and destructors, Inline functions, Static class members, This pointer, Friend functions, Dynamic memory allocation and deallocation (new and delete).Polymorphism and InheritanceFunction overloading, Operator overloading, Generic programming function and class templates, Inheritance basics, Base and derived classes, Different types of inheritance, Base class access control, Virtual base class, Function overriding, Run time polymorphism using virtual functions, Abstract classes.Streams, Libraries and error handling-stream classes hierarchy, Console I/O, Formatted I/O, File streams and string streams, Exception handling mechanism, Standard template library.Algorithms, Performance analysis-time complexity and space complexity, Review of basic data structures,The list ADT, Stack ADT, Implementation using template class in C++, Queue ADT, Implementation using template class, Priority queues-definition, ADT, Heaps, Definition, Insertion and deletion, Application-heap sort, Disjoint sets-disjoint set ADT, Disjoint set operations, Union and find algorithms.Skip lists and hashingDictionaries, Linear list representation, Skip list representation, Operations-insertion, Deletion and searching, Hash table representation, Hash functions, Collision resolution- separate chaining, Open addressing-linear probing, Quadratic probing, Double hashing, Rehashing, Extendible hashing, Comparison of hashing and skip lists.Search trees (Part I)Binary search trees, definition, ADT, Implementation, Operations searching, Insertion and deletion, Balanced search trees-AVL trees, Definition, Height of an AVL tree, Representation, operations-insertion, Deletion and searching.Search trees (Part II)Red-black trees representation, Insertion, Deletion, Searching splay trees introduction, The splay operation, B-Trees, B-Tree of order m, Height of a B-Tree, Insertion deletion and searching.Pattern matching and triesPattern matching algorithms-Brute force, The Boyer-Moore algorithm, The Knuth-Morris-Pratt algorithm, Standard tries, Compressed tries, Suffix trees, Search engines-inverted files.

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Learning Opencv

Learning OpenCV 3
by Adrian Kaehler, Gary Bradski

Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. You’ll learn what it takes to build applications that enable computers to “see” and make decisions based on that data.

With over 500 functions that span many areas in vision, OpenCV is used for commercial applications such as security, medical imaging, pattern and face recognition, robotics, and factory product inspection. This book gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated vision applications. Hands-on exercises in each chapter help you apply what you’ve learned.

This volume covers the entire library, in its modern C++ implementation, including machine learning tools for computer vision.

  • Learn OpenCV data types, array types, and array operations
  • Capture and store still and video images with HighGUI
  • Transform images to stretch, shrink, warp, remap, and repair
  • Explore pattern recognition, including face detection
  • Track objects and motion through the visual field
  • Reconstruct 3D images from stereo vision
  • Discover basic and advanced machine learning techniques in OpenCV

Learning OpenCV 3
by Adrian Kaehler, Gary Bradski

Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. You’ll learn what it takes to build applications that enable computers to “see” and make decisions based on that data.

With over 500 functions that span many areas in vision, OpenCV is used for commercial applications such as security, medical imaging, pattern and face recognition, robotics, and factory product inspection. This book gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated vision applications. Hands-on exercises in each chapter help you apply what you’ve learned.

This volume covers the entire library, in its modern C++ implementation, including machine learning tools for computer vision.

  • Learn OpenCV data types, array types, and array operations
  • Capture and store still and video images with HighGUI
  • Transform images to stretch, shrink, warp, remap, and repair
  • Explore pattern recognition, including face detection
  • Track objects and motion through the visual field
  • Reconstruct 3D images from stereo vision
  • Discover basic and advanced machine learning techniques in OpenCV

Learning OpenCV
by Gary Bradski, Adrian Kaehler

“This library is useful for practitioners, and is an excellent tool for those entering the field: it is a set of computer vision algorithms that work as advertised.”-William T. Freeman, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology

Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to “see” and make decisions based on that data.

Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time.

Learning OpenCV will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter. This book includes:

  • A thorough introduction to OpenCV
  • Getting input from cameras
  • Transforming images
  • Segmenting images and shape matching
  • Pattern recognition, including face detection
  • Tracking and motion in 2 and 3 dimensions
  • 3D reconstruction from stereo vision
  • Machine learning algorithms

Getting machines to see is a challenging but entertaining goal. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book you need to get started.


Machine Learning for OpenCV
by Michael Beyeler

Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide.

About This Book

  • Load, store, edit, and visualize data using OpenCV and Python
  • Grasp the fundamental concepts of classification, regression, and clustering
  • Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide
  • Evaluate, compare, and choose the right algorithm for any task

Who This Book Is For

This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks.

What You Will Learn

  • Explore and make effective use of OpenCV’s machine learning module
  • Learn deep learning for computer vision with Python
  • Master linear regression and regularization techniques
  • Classify objects such as flower species, handwritten digits, and pedestrians
  • Explore the effective use of support vector machines, boosted decision trees, and random forests
  • Get acquainted with neural networks and Deep Learning to address real-world problems
  • Discover hidden structures in your data using k-means clustering
  • Get to grips with data pre-processing and feature engineering

In Detail

Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today’s most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind.

OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for.

Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today’s hottest topic in the field: Deep Learning.

By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!

Style and approach

OpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models.


Learning OpenCV 3 Application Development
by Samyak Datta

Build, create, and deploy your own computer vision applications with the power of OpenCV

About This Book

  • This book provides hands-on examples that cover the major features that are part of any important Computer Vision application
  • It explores important algorithms that allow you to recognize faces, identify objects, extract features from images, help your system make meaningful predictions from visual data, and much more
  • All the code examples in the book are based on OpenCV 3.1 – the latest version

Who This Book Is For

This is the perfect book for anyone who wants to dive into the exciting world of image processing and computer vision. This book is aimed at programmers with a working knowledge of C++. Prior knowledge of OpenCV or Computer Vision/Machine Learning is not required.

What You Will Learn

  • Explore the steps involved in building a typical computer vision/machine learning application
  • Understand the relevance of OpenCV at every stage of building an application
  • Harness the vast amount of information that lies hidden in images into the apps you build
  • Incorporate visual information in your apps to create more appealing software
  • Get acquainted with how large-scale and popular image editing apps such as Instagram work behind the scenes by getting a glimpse of how the image filters in apps can be recreated using simple operations in OpenCV
  • Appreciate how difficult it is for a computer program to perform tasks that are trivial for human beings
  • Get to know how to develop applications that perform face detection, gender detection from facial images, and handwritten character (digit) recognition

In Detail

Computer vision and machine learning concepts are frequently used in practical computer vision based projects. If you’re a novice, this book provides the steps to build and deploy an end-to-end application in the domain of computer vision using OpenCV/C++.

At the outset, we explain how to install OpenCV and demonstrate how to run some simple programs. You will start with images (the building blocks of image processing applications), and see how they are stored and processed by OpenCV. You’ll get comfortable with OpenCV-specific jargon (Mat Point, Scalar, and more), and get to know how to traverse images and perform basic pixel-wise operations.

Building upon this, we introduce slightly more advanced image processing concepts such as filtering, thresholding, and edge detection. In the latter parts, the book touches upon more complex and ubiquitous concepts such as face detection (using Haar cascade classifiers), interest point detection algorithms, and feature descriptors. You will now begin to appreciate the true power of the library in how it reduces mathematically non-trivial algorithms to a single line of code!

The concluding sections touch upon OpenCV’s Machine Learning module. You will witness not only how OpenCV helps you pre-process and extract features from images that are relevant to the problems you are trying to solve, but also how to use Machine Learning algorithms that work on these features to make intelligent predictions from visual data!

Style and approach

This book takes a very hands-on approach to developing an end-to-end application with OpenCV. To avoid being too theoretical, the description of concepts are accompanied simultaneously by the development of applications. Throughout the course of the book, the projects and practical, real-life examples are explained and developed step by step in sync with the theory.


Learning OpenCV 3 Computer Vision with Python
by Joe Minichino, Joseph Howse

Unleash the power of computer vision with Python using OpenCV

About This Book

  • Create impressive applications with OpenCV and Python
  • Familiarize yourself with advanced machine learning concepts
  • Harness the power of computer vision with this easy-to-follow guide

Who This Book Is For

Intended for novices to the world of OpenCV and computer vision, as well as OpenCV veterans that want to learn about what’s new in OpenCV 3, this book is useful as a reference for experts and a training manual for beginners, or for anybody who wants to familiarize themselves with the concepts of object classification and detection in simple and understandable terms. Basic knowledge about Python and programming concepts is required, although the book has an easy learning curve both from a theoretical and coding point of view.

What You Will Learn

  • Install and familiarize yourself with OpenCV 3’s Python API
  • Grasp the basics of image processing and video analysis
  • Identify and recognize objects in images and videos
  • Detect and recognize faces using OpenCV
  • Train and use your own object classifiers
  • Learn about machine learning concepts in a computer vision context
  • Work with artificial neural networks using OpenCV
  • Develop your own computer vision real-life application

In Detail

OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV’s API will enable the development of all sorts of real-world applications, including security and surveillance.

Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application.

Style and approach

This book is a comprehensive guide to the brand new OpenCV 3 with Python to develop real-life computer vision applications.


Learning Image Processing with OpenCV
by Gloria Bueno García, Oscar Deniz Suarez, José Luis Espinosa Aranda, Jesus Salido Tercero, Ismael Serrano Gracia, Noelia Vállez Enano

If you are a competent C++ programmer and want to learn the tricks of image processing with OpenCV, then this book is for you. A basic understanding of image processing is required.

Learning OpenCV
by Gary Bradski, Adrian Kaehler

Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to “see” and make decisions based on that data.

The second edition is updated to cover new features and changes in OpenCV 2.0, especially the C++ interface.

Computer vision is everywhere—in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book any developer or hobbyist needs to get started, with the help of hands-on exercises in each chapter.

This book includes:

  • A thorough introduction to OpenCV
  • Getting input from cameras
  • Transforming images
  • Segmenting images and shape matching
  • Pattern recognition, including face detection
  • Tracking and motion in 2 and 3 dimensions
  • 3D reconstruction from stereo vision
  • Machine learning algorithms

Learn Computer Vision Using OpenCV
by Sunila Gollapudi

Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer vision such as image and object detection, tracking and motion analysis and their applications with examples.
The author starts with an introduction to computer vision followed by setting up OpenCV from scratch using Python. The next section discusses specialized image processing and segmentation and how images are stored and processed by a computer. This involves pattern recognition and image tagging using the OpenCV library. Next, you’ll work with object detection, video storage and interpretation, and human detection using OpenCV. Tracking and motion is also discussed in detail. The book also discusses creating complex deep learning models with CNN and RNN. The author finally concludes with recent applications and trends in computer vision.
After reading this book, you will be able to understand and implement computer vision and its applications with OpenCV using Python. You will also be able to create deep learning models with CNN and RNN and understand how these cutting-edge deep learning architectures work.
What You Will Learn

  • Understand what computer vision is, and its overall application in intelligent automation systems
  • Discover the deep learning techniques required to build computer vision applications
  • Build complex computer vision applications using the latest techniques in OpenCV, Python, and NumPy
  • Create practical applications and implementations such as face detection and recognition, handwriting recognition, object detection, and tracking and motion analysis


Who This Book Is ForThose who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications.


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Soft Computing & Intelligent Systems

Analysis and Design of Intelligent Systems Using Soft Computing Techniques
by Patricia Melin, Oscar Castillo, Eduardo G. Ramírez, Witold Pedrycz

This book comprises a selection of papers from IFSA 2007 on new methods for ana- sis and design of hybrid intelligent systems using soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including fuzzy logic, n- ral networks, and genetic algorithms, which can be used to produce powerful hybrid intelligent systems for solving problems in pattern recognition, time series prediction, intelligent control, robotics and automation. Hybrid intelligent systems that combine several SC techniques are needed due to the complexity and high dimensionality of real-world problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of these systems, for this reason it is very important to optimize architecture design. The architectures can combine, in different ways, neural networks, fuzzy logic and genetic algorithms, to achieve the ultimate goal of pattern recognition, time series prediction, intelligent control, or other application areas. This book is intended to be a major reference for scientists and engineers interested in applying new computational and mathematical tools to design hybrid intelligent systems. This book can also be used as a reference for graduate courses like the f- lowing: soft computing, intelligent pattern recognition, computer vision, applied ar- ficial intelligence, and similar ones. The book is divided in to twelve main parts. Each part contains a set of papers on a common subject, so that the reader can find similar papers grouped together.

Soft Computing and Intelligent Systems Design
by Fakhreddine Karray, Fakhreddine O. Karray, Clarence W. De Silva

Traditional artificial intelligence (AI) techniques are based around mathematical techniques of symbolic logic, with programming in languages such as Prolog and LISP invented in the 1960s. These are referred to as “crisp” techniques by the soft computing community. The new wave of AI methods seeks inspiration from the world of biology, and is being used to create numerous real-world intelligent systems with the aid of soft computing tools. These new methods are being increasingly taught at the upper end of the curriculum, sometimes as an adjunct to traditional AI courses, and sometimes as a replacement for them. Where a more radical approach is taken and the course is being taught at an introductory level, we have recently published Negnevitsky’s book. Karray and Silva will be suitable for the majority of courses which will be found at an advanced level. Karray and de Silva cover the problem of control and intelligent systems design using soft-computing techniques in an integrated manner. They present both theory and applications, including industrial applications, and the book contains numerous worked examples, problems and case studies. Covering the state-of-the-art in soft-computing techniques, the book gives the reader sufficient knowledge to tackle a wide range of complex systems for which traditional techniques are inadequate.


Intelligent Systems and Soft Computing
by Behnam Azvine, Nader Azarmi, Detlef D. Nauck

Artificial intelligence has, traditionally focused on solving human-centered problems like natural language processing or common-sense reasoning. On the other hand, for a while now soft computing has been applied successfully in areas like pattern recognition, clustering, or automatic control. The papers in this book explore the possibility of bringing these two areas together.
This book is unique in the way it concentrates on building intelligent software systems by combining methods from diverse disciplines, such as fuzzy set theory, neuroscience, agent technology, knowledge discovery, and symbolic artificial intelligence. The first part of the book focuses on foundational aspects and future directions; the second part provides the reader with an overview of recently developed software tools for building flexible intelligent systems; the final section studies developed applications in various fields.

Soft Computing and Intelligent Systems
by Madan M. Gupta

The field of soft computing is emerging from the cutting edge research over the last ten years devoted to fuzzy engineering and genetic algorithms. The subject is being called soft computing and computational intelligence. With acceptance of the research fundamentals in these important areas, the field is expanding into direct applications through engineering and systems science.

This book cover the fundamentals of this emerging filed, as well as direct applications and case studies. There is a need for practicing engineers, computer scientists, and system scientists to directly apply “fuzzy” engineering into a wide array of devices and systems.


Soft Computing and Intelligent Systems
by Madan M. Gupta

Outline of a computational theory of perceptions based on computing with words / L.A. Zadeh — Introduction to soft computing and intelligent control systems / N.K. Sinha and M.M. Gupta — Computational issues in intelligent control / X.D. Koutsoukos and P.J. Antsaklis — Neural networks — a guided tour / S. Haykin — On generating variable structure organization using a genetic algorithm / A.K. Zaidi and A.H. Levis — Evolutionary algorithms and neural networks / R.G.S. Asthana — Neural networks and fuzzy systems / P. Musilek and M.M. Gupta — Fuzzy neural networks / P. Musilek and M.M. Gupta — A cursory look at parallel and distributed architectures and biologically inspired computing / S.K. Basu — Developments in learning control systems / J.X. Xu … [et al.] — Techniques for genetic adaptive control / W.K. Lennon and K.M. Passino — Cooperative behavior of intelligent agents : theory and practice / L. Vlacic, A. Engwirda, and M. Kajitani — Expert systems in process diagnosis …

Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing
by Patricia Melin, Oscar Castillo

This monograph describes new methods for intelligent pattern recognition using soft computing techniques including neural networks, fuzzy logic, and genetic algorithms. Hybrid intelligent systems that combine several soft computing techniques are needed due to the complexity of pattern recognition problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of pattern recognition systems, to achieve the ultimate goal of pattern recognition. This book also shows results of the application of hybrid intelligent systems to real-world problems of face, fingerprint, and voice recognition. This monograph is intended to be a major reference for scientists and engineers applying new computational and mathematical tools to intelligent pattern recognition and can be also used as a textbook for graduate courses in soft computing, intelligent pattern recognition, computer vision, or applied artificial intelligence.


Handbook of Research on Novel Soft Computing Intelligent Algorithms
by Pandian Vasant

As technologies grow more complex, modeling and simulation of new intelligent systems becomes increasingly challenging and nuanced; specifically in diverse fields such as medicine, engineering, and computer science. Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications explores emerging technologies and best practices to effectively address concerns inherent in properly optimizing advanced systems. With applications in areas such as bio-engineering, space exploration, industrial informatics, information security, and nuclear and renewable energies, this exceptional reference will serve as an important tool for decision makers, managers, researchers, economists, and industrialists across a wide range of scientific fields.

International Proceedings on Advances in Soft Computing, Intelligent Systems and Applications
by M. Sreenivasa Reddy, K. Viswanath, Shiva Prasad K.M.

The book focuses on the state-of-the-art technologies pertaining to advances in soft computing, intelligent system and applications. The Proceedings of ASISA 2016 presents novel and original work in soft computing, intelligent system and applications by the experts and budding researchers. These are the cutting edge technologies that have immense application in various fields. The papers discuss many real world complex problems that cannot be easily handled with traditional mathematical methods. The exact solution of the problems at hand can be achieved with soft computing techniques. Soft computing represents a collection of computational techniques inheriting inspiration from evolutionary algorithms, nature inspired algorithms, bio-inspired algorithms, neural networks and fuzzy logic.


Soft Computing Based Modeling in Intelligent Systems
by Valentina Emilia Balas, János Fodor, Annamária R. Várkonyi-Kóczy

The book “Soft Computing Based Modeling in Intelligent Systems”contains the – tended works originally presented at the IEEE International Workshop SOFA 2005 and additional papers. SOFA, an acronym for SOFt computing and Applications, is an international wo- shop intended to advance the theory and applications of intelligent systems and soft computing. Lotfi Zadeh, the inventor of fuzzy logic, has suggested the term “Soft Computing.” He created the Berkeley Initiative of Soft Computing (BISC) to connect researchers working in these new areas of AI. Professor Zadeh participated actively in our wo- shop. Soft Computing techniques are tolerant to imprecision, uncertainty and partial truth. Due to the large variety and complexity of the domain, the constituting methods of Soft Computing are not competing for a comprehensive ultimate solution. Instead they are complementing each other, for dedicated solutions adapted to each specific pr- lem. Hundreds of concrete applications are already available in many domains. Model based approaches offer a very challenging way to integrate a priori knowledge into procedures. Due to their flexibility, robustness, and easy interpretability, the soft c- puting applications will continue to have an exceptional role in our technologies. The applications of Soft Computing techniques in emerging research areas show its mat- ity and usefulness. The IEEE International Workshop SOFA 2005 held Szeged-Hungary and Arad- Romania in 2005 has led to the publication of these two edited volumes. This volume contains Soft Computing methods and applications in modeling, optimisation and prediction.