Search results for "handbook of computer programming with python"
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Introduction to Programming in Java
Programming skills are indispensable in today’s world, not just for computer science students, but also for anyone in any scientific or technical discipline. An Introduction to Programming in Java, Second Edition, by Robert Sedgewick and Kevin Wayne is an accessible, interdisciplinary treatment that emphasizes important and engaging applications, not toy problems. The authors supply the tools needed for students and professionals to learn that programming is a natural, satisfying, and creative experience, and to become conversant with one of the world’s most widely used languages. This example-driven guide focuses on Java’s most useful features and brings programming to life for every student in the sciences, engineering, and computer science. Coverage includes Basic elements of programming: variables, assignment statements, built-in data types, conditionals, loops, arrays, and I/O, including graphics and soundFunctions, modules, and libraries: organizing programs into components that can be independently debugged, maintained, and reusedAlgorithms and data structures: sort/search algorithms, stacks, queues, and symbol tablesApplications from applied math, physics, chemistry, biology, and computer science Drawing on their extensive classroom experience, the authors provide Q&As, exercises, and opportunities for creative engagement with the material throughout. Together with the companion materials, this book empowers people pursue a modern approach to teaching and learning programming. Companion web site contains Chapter summariesSupplementary exercises, some with solutionsDetailed instructions for installing a Java programming environmentProgram code and test data suitable for easy downloadDetailed creative exercises, projects, and other supplementary mat And companion studio-produced online videos provide students and professionals with the opportunity to engage with the material at their own pace and instructors the opportunity to spend their time with students helping them to succeed on assignments and exams.
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Handbook of Genetic Programming Applications
This contributed volume, written by leading international researchers, reviews the latest developments of genetic programming (GP) and its key applications in solving current real world problems, such as energy conversion and management, financial analysis, engineering modeling and design, and software engineering, to name a few. Inspired by natural evolution, the use of GP has expanded significantly in the last decade in almost every area of science and engineering. Exploring applications in a variety of fields, the information in this volume can help optimize computer programs throughout the sciences. Taking a hands-on approach, this book provides an invaluable reference to practitioners, providing the necessary details required for a successful application of GP and its branches to challenging problems ranging from drought prediction to trading volatility. It also demonstrates the evolution of GP through major developments in GP studies and applications. It is suitable for advanced students who wish to use relevant book chapters as a basis to pursue further research in these areas, as well as experienced practitioners looking to apply GP to new areas. The book also offers valuable supplementary material for design courses and computation in engineering.
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GitHub Copilot Handbook
Discover the transformational impact of GitHub Copilot, from speeding up delivery, cutting grunt work, and transforming how your team codes, reviews, and ships software using the AI-powered features.Key FeaturesAutomate code generation, test writing, and debugging with AI across your dev workflowExplore GitHub Copilot’s advanced features like PR reviews, error insights, and Copilot ChatScale Copilot across dev, QA, and PM roles with guided onboarding and practical usage patternsBook DescriptionCross-functional product teams are under constant pressure to build and ship faster, but too much time is lost to manual coding, slow reviews, and fragmented workflows. GitHub Copilot streamlines day-to-day coding so your team can focus on delivering value to users while maintaining high quality on real projects.Written by industry experts Rob Bos and Randy Pagels, this book shows how GitHub Copilot supports your work from start to finish. You’ll learn how to turn ideas into tasks, write code with fewer hiccups, spot problems earlier, and understand errors when things go wrong. You’ll also see how Copilot suggests improvements in pull requests and helps fix common build issues, enabling teams to keep moving and ship with confidence.You’ll integrate GitHub Copilot into daily routines, share it across roles, and make it stick with simple checklists and clear examples. You’ll also track what works, set guardrails, and build an internal community.By the end, you’ll know when GitHub Copilot helps – and when it doesn’t – and you’ll be ready to write, review, and ship code with confidence on real projects.What you will learnApply GitHub Copilot across the full software development life cycleUnderstand how AI powers the suggestions and chat featuresBoost productivity by automating tests, reviews, and pipeline fixesIntegrate Copilot into IDEs and GitHub.com for maximum valueRoll out Copilot across teams with proven onboarding strategiesBuild a knowledge-sharing culture with Copilot community championsWho this book is forThis book is for anyone who works with application creation and who wants to harness GitHub Copilot in real-world coding environments. Whether you’re a beginner looking to speed up learning or a seasoned engineer aiming to boost productivity, this guide shows how to put Copilot to work effectively.Software engineers, DevOps professionals, QA specialists, and tech leads will discover how to streamline coding, reviews, and delivery with AI-assisted workflows. Product managers and other collaborators will also gain insight into how, with GitHub Copilot, they themselves can leverage AI.
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Electromagnetic Simulation Using the FDTD Method with Python
Provides an introduction to the Finite Difference Time Domain method and shows how Python code can be used to implement various simulations This book allows engineering students and practicing engineers to learn the finite-difference time-domain (FDTD) method and properly apply it toward their electromagnetic simulation projects. Each chapter contains a concise explanation of an essential concept and instruction on its implementation into computer code. Included projects increase in complexity, ranging from simulations in free space to propagation in dispersive media. This third edition utilizes the Python programming language, which is becoming the preferred computer language for the engineering and scientific community. Electromagnetic Simulation Using the FDTD Method with Python, Third Edition is written with the goal of enabling readers to learn the FDTD method in a manageable amount of time. Some basic applications of signal processing theory are explained to enhance the effectiveness of FDTD simulation. Topics covered in include one-dimensional simulation with the FDTD method, two-dimensional simulation, and three-dimensional simulation. The book also covers advanced Python features and deep regional hyperthermia treatment planning. Electromagnetic Simulation Using the FDTD Method with Python: Guides the reader from basic programs to complex, three-dimensional programs in a tutorial fashionIncludes a rewritten fifth chapter that illustrates the most interesting applications in FDTD and the advanced graphics techniques of PythonCovers peripheral topics pertinent to time-domain simulation, such as Z-transforms and the discrete Fourier transformProvides Python simulation programs on an accompanying website An ideal book for senior undergraduate engineering students studying FDTD, Electromagnetic Simulation Using the FDTD Method with Python will also benefit scientists and engineers interested in the subject.
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Bioinformatics with Python Cookbook
Discover modern, next-generation sequencing libraries from Python ecosystem to analyze large amounts of biological dataKey FeaturesPerform complex bioinformatics analysis using the most important Python libraries and applicationsImplement next-generation sequencing, metagenomics, automating analysis, population genetics, and moreExplore various statistical and machine learning techniques for bioinformatics data analysisBook DescriptionBioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data.This book covers next-generation sequencing, genomics, metagenomics, population genetics, phylogenetics, and proteomics. You'll learn modern programming techniques to analyze large amounts of biological data. With the help of real-world examples, you'll convert, analyze, and visualize datasets using various Python tools and libraries.This book will help you get a better understanding of working with a Galaxy server, which is the most widely used bioinformatics web-based pipeline system. This updated edition also includes advanced next-generation sequencing filtering techniques. You'll also explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks such as Dask and Spark.By the end of this book, you'll be able to use and implement modern programming techniques and frameworks to deal with the ever-increasing deluge of bioinformatics data.What you will learnLearn how to process large next-generation sequencing (NGS) datasetsWork with genomic dataset using the FASTQ, BAM, and VCF formatsLearn to perform sequence comparison and phylogenetic reconstructionPerform complex analysis with protemics dataUse Python to interact with Galaxy serversUse High-performance computing techniques with Dask and SparkVisualize protein dataset interactions using CytoscapeUse PCA and Decision Trees, two machine learning techniques, with biological datasetsWho this book is forThis book is for Data data Scientistsscientists, Bioinformatics bioinformatics analysts, researchers, and Python developers who want to address intermediate-to-advanced biological and bioinformatics problems using a recipe-based approach. Working knowledge of the Python programming language is expected.
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Methods of Programming
The systematic development of software systems is a centraltask of computing science. A software system is the resultof putting together knowledge about the application, therequirements and the structures of computing science.Under the heading CIP (Computer-aided Intuition-guidedProgramming), a group of researchers led by Prof. F.L. Bauerand Prof. K. Samelson started work in 1975 in the directionof formal program specification, transformationalprogramming, and tool supportfor program development.The collection of papers in this volume presents examples ofa formal approach to programming language concepts andprogram development based on algebraic specifications andprogram transformations. Examples are also presented ofevolutions and modificationsof the original ideas of theCIP project. The topics range from descriptionsof theprogram development process to derivations of algorithmsfrom specifications.The volume is dedicated to Prof. F.L. Bauer.
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Thermal Physics Tutorials with Python Simulations
This book provides an accessible introduction to thermal physics with computational approaches that complement the traditional mathematical treatments of classical thermodynamics and statistical mechanics. It guides readers through visualizations and simulations in the Python programming language, helping them to develop their own technical computing skills (including numerical and symbolic calculations, optimizations, recursive operations, and visualizations). Python is a highly readable and practical programming language, making this book appropriate for students without extensive programming experience.This book may serve as a thermal physics textbook for a semester-long undergraduate thermal physics course or may be used as a tutorial on scientific computing with focused examples from thermal physics. This book will also appeal to engineering students studying intermediate-level thermodynamics as well as computer science students looking to understand how to apply their computer programming skills to science.Key features Major concepts in thermal physics are introduced cohesively through computational and mathematical treatments. Computational examples in Python programming language guide students on how to simulate and visualize thermodynamic principles and processes for themselves.
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Hands-On Web Scraping with Python
Work through practical examples to unlock the full potential of web scraping with Python and gain valuable insights from high-quality dataKey FeaturesBuild an initial portfolio of web scraping projects with detailed explanationsGrasp Python programming fundamentals related to web scraping and data extractionAcquire skills to code web scrapers, store data in desired formats, and employ the data professionallyPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWeb scraping is a powerful tool for extracting data from the web, but it can be daunting for those without a technical background. Designed for novices, this book will help you grasp the fundamentals of web scraping and Python programming, even if you have no prior experience.Adopting a practical, hands-on approach, this updated edition of Hands-On Web Scraping with Python uses real-world examples and exercises to explain key concepts. Starting with an introduction to web scraping fundamentals and Python programming, you’ll cover a range of scraping techniques, including requests, lxml, pyquery, Scrapy, and Beautiful Soup. You’ll also get to grips with advanced topics such as secure web handling, web APIs, Selenium for web scraping, PDF extraction, regex, data analysis, EDA reports, visualization, and machine learning.This book emphasizes the importance of learning by doing. Each chapter integrates examples that demonstrate practical techniques and related skills. By the end of this book, you’ll be equipped with the skills to extract data from websites, a solid understanding of web scraping and Python programming, and the confidence to use these skills in your projects for analysis, visualization, and information discovery.What you will learnMaster web scraping techniques to extract data from real-world websitesImplement popular web scraping libraries such as requests, lxml, Scrapy, and pyqueryDevelop advanced skills in web scraping, APIs, PDF extraction, regex, and machine learningAnalyze and visualize data with Pandas and PlotlyDevelop a practical portfolio to demonstrate your web scraping skillsUnderstand best practices and ethical concerns in web scraping and data extractionWho this book is forThis book is for beginners who want to learn web scraping and data extraction using Python. No prior programming knowledge is required, but a basic understanding of web-related concepts such as websites, browsers, and HTML is assumed. If you enjoy learning by doing and want to build a portfolio of web scraping projects and delve into data-related studies and application, then this book is tailored for your needs.
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Mobile Phone Programming
This book provides a solid overview of mobile phone programming for readers in both academia and industry. Coverage includes all commercial realizations of the Symbian, Windows Mobile and Linux platforms. The text introduces each programming language (JAVA, Python, C/C++) and offers a set of development environments "step by step," to help familiarize developers with limitations, pitfalls, and challenges.
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Algorithmic Decision Making with Python Resources
This book describes Python3 programming resources for implementing decision aiding algorithms in the context of a bipolar-valued outranking approach. These computing resources, made available under the name Digraph3, are useful in the field of Algorithmic Decision Theory and more specifically in outranking-based Multiple-Criteria Decision Aiding (MCDA).The first part of the book presents a set of tutorials introducing the Digraph3 collection of Python3 modules and its main objects, such as bipolar-valued digraphs and outranking digraphs. In eight methodological chapters, the second part illustrates multiple-criteria evaluation models and decision algorithms. These chapters are largely problem-oriented and demonstrate how to edit a new multiple-criteria performance tableau, how to build a best choice recommendation, how to compute the winner of an election and how to make rankings or ratings using incommensurable criteria. The book’s third part presents three real-world decision case studies, while the fourth part addresses more advanced topics, such as computing ordinal correlations between bipolar-valued outranking digraphs, computing kernels in bipolar-valued digraphs, testing for confidence or stability of outranking statements when facing uncertain or solely ordinal criteria significance weights, and tempering plurality tyranny effects in social choice problems. The fifth and last part is more specifically focused on working with undirected graphs, tree graphs and forests. The closing chapter explores comparability, split, interval and permutation graphs.The book is primarily intended for graduate students in management sciences, computational statistics and operations research. The chapters presenting algorithms for ranking multicriteria performance records will be of computational interest for designers of web recommender systems. Similarly, the relative and absolute quantile-rating algorithms, discussed and illustrated in several chapters, will be of practical interest to public and private performance auditors.
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Getting Started with Python Data Analysis
Learn to use powerful Python libraries for effective data processing and analysisAbout This Book• Learn the basic processing steps in data analysis and how to use Python in this area through supported packages, especially Numpy, Pandas, and Matplotlib• Create, manipulate, and analyze your data to extract useful information to optimize your system• A hands-on guide to help you learn data analysis using PythonWho This Book Is ForIf you are a Python developer who wants to get started with data analysis and you need a quick introductory guide to the python data analysis libraries, then this book is for you.What You Will Learn• Understand the importance of data analysis and get familiar with its processing steps• Get acquainted with Numpy to use with arrays and array-oriented computing in data analysis• Create effective visualizations to present your data using Matplotlib• Process and analyze data using the time series capabilities of Pandas• Interact with different kind of database systems, such as file, disk format, Mongo, and Redis• Apply the supported Python package to data analysis applications through examples• Explore predictive analytics and machine learning algorithms using Scikit-learn, a Python libraryIn DetailData analysis is the process of applying logical and analytical reasoning to study each component of data. Python is a multi-domain, high-level, programming language. It's often used as a scripting language because of its forgiving syntax and operability with a wide variety of different eco-systems. Python has powerful standard libraries or toolkits such as Pylearn2 and Hebel, which offers a fast, reliable, cross-platform environment for data analysis.With this book, we will get you started with Python data analysis and show you what its advantages are.The book starts by introducing the principles of data analysis and supported libraries, along with NumPy basics for statistic and data processing. Next it provides an overview of the Pandas package and uses its powerful features to solve data processing problems.Moving on, the book takes you through a brief overview of the Matplotlib API and some common plotting functions for DataFrame such as plot. Next, it will teach you to manipulate the time and data structure, and load and store data in a file or database using Python packages. The book will also teach you how to apply powerful packages in Python to process raw data into pure and helpful data using examples.Finally, the book gives you a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or build helpful products, such as recommendations and predictions using scikit-learn. Style and approachThis is an easy-to-follow, step-by-step guide to get you familiar with data analysis and the libraries supported by Python. Topics are explained with real-world examples wherever required.
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Foundations for Analytics with Python
If you're like many of Excel's 750 million users, you want to do more with your data-like repeating similar analyses over hundreds of files, or combining data in many files for analysis at one time. This practical guide shows ambitious non-programmers how to automate and scale the processing and analysis of data in different formats-by using Python. After author Clinton Brownley takes you through Python basics, you'll be able to write simple scripts for processing data in spreadsheets as well as databases. You'll also learn how to use several Python modules for parsing files, grouping data, and producing statistics. No programming experience is necessary.Create and run your own Python scripts by learning basic syntax Use Python's csv module to read and parse CSV files Read multiple Excel worksheets and workbooks with the xlrd module Perform database operations in MySQL or with the mysqlclient module Create Python applications to find specific records, group data, and parse text files Build statistical graphs and plots with matplotlib, pandas, ggplot, and seaborn Produce summary statistics, and estimate regression and classification models Schedule your scripts to run automatically in both Windows and Mac environments
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Computer Science Programming Basics with Ruby
This book is intended as a text for an "Introduction to Computer Science" course for non-majors. That is, this book is intended for "the rest of the world"; namely "every can program". No specific pre-requisites at the college level are needed. However, basic high school level mathematics knowledge is assumed. Our approach minimizes the need to harp on the illustrative programming language details in favor of an immediate introduction to core computer science topics. User interface issues are ignored as these have little if anything to do with the foundations of Computer Science. We use a very practical programming language (Ruby) as the foundation and provide a variety of detailed examples using the language that illustrate the concepts being taught.
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Programming Languages: Principles and Paradigms
This textbook is a thorough, up-to-date introduction to the principles and techniques that guide the design and implementation of modern programming languages. The goal of the book is to provide the basis for a critical understanding of most modern programming languages. Thus, rather than focusing on a specific language, the book identifies the most important principles shared by large classes of languages. The notion of ‘abstract machine’ is a unifying concept that helps to maintain an accurate and elementary treatment. The book introduces, analyses in depth, and compares the imperative, object-oriented, functional, logic, concurrent, constraint-based, and service-oriented programming paradigms. All material coming from the first English edition has been updated and extended, clarifying some tricky points, and discussing newer programming languages. This second edition contains new chapters dedicated to constraint, concurrent, and service-oriented programming.Topics and features:Requires familiarity with one programming language is a prerequisiteProvides a chapter on history offering context for most of the constructs in use todayPresents an elementary account of semantical approaches and of computabilityIntroduces new examples in modern programming languages like Python or ScalaOffers a chapter that opens a perspective on applications in artificial intelligenceConceived as a university textbook, this unique volume will also be suitable for IT specialists who want to deepen their knowledge of the mechanisms behind the languages they use. The choice of themes and the presentation style are largely influenced by the experience of teaching the content as part of a bachelor's degree in computer science.
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Guide to Programming and Algorithms Using R
This easy-to-follow textbook provides a student-friendly introduction to programming and algorithms. Emphasis is placed on the threshold concepts that present barriers to learning, including the questions that students are often too embarrassed to ask. The book promotes an active learning style in which a deeper understanding is gained from evaluating, questioning, and discussing the material, and practised in hands-on exercises. Although R is used as the language of choice for all programs, strict assumptions are avoided in the explanations in order for these to remain applicable to other programming languages. Features: provides exercises at the end of each chapter; includes three mini projects in the final chapter; presents a list of titles for further reading at the end of the book; discusses the key aspects of loops, recursions, program and algorithm efficiency and accuracy, sorting, linear systems of equations, and file processing; requires no prior background knowledge in this area.
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Programming Massively Parallel Processors
Programming Massively Parallel Processors: A Hands-on Approach, Fifth Edition shows both students and professionals alike the basic concepts of parallel programming and GPU architecture. Concise, intuitive, and practical, it is based on years of road-testing in the authors' own parallel computing courses. Various techniques for constructing and optimizing parallel programs are explored in detail, while case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This new edition has been updated with an expanded repertoire of optimizations, new patterns and applications, ad more coverage of important CUDA features.
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Introduction to Scientific Computing with MATLAB® and Python Tutorials
This textbook is written for the first introductory course on scientific computing. It covers elementary numerical methods for linear systems, root finding, interpolation, numerical integration, numerical differentiation, least squares problems, initial value problems and boundary value problems. It includes short Matlab and Python tutorials to quickly get students started on programming. It makes the connection between elementary numerical methods with advanced topics such as machine learning and parallel computing.This textbook gives a comprehensive and in-depth treatment of elementary numerical methods. It balances the development, implementation, analysis and application of a fundamental numerical method by addressing the following questions.•Where is the method applied?•How is the method developed?•How is the method implemented?•How well does the method work?The material in the textbook is made as self-contained and easy-to-follow as possible with reviews and remarks. The writing is kept concise and precise. Examples, figures, paper-and-pen exercises and programming problems are deigned to reinforce understanding of numerical methods and problem-solving skills.
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Handbook of Logic in Artificial Intelligence and Logic Programming: Volume 2: Deduction Methodologies
Logic is now widely recognized to be one of the foundational disciplines of computing and has found applications in virtually all aspects of the subject, from software engineering and hardware to programming languages and artificial intelligence. There is a growing need for an in-depth survey of the applications of logic in Al and computer science. The Handbook of Logic in Articial Intelligence and Logic Programming and its companion, the Handbook of Logic in Computer Science, have been created in response to this need. We see the creation of the Handbook as a combination of authoritative exposition, comprehensive survey, and fundamental research exploring the underlying themes in the various areas. The intended audience is graduate students and researchers in the areas of A1 and logic, as well as other people interested in the subject. We assume as background some mathematical sophistication. Much of the material will be of interest to logicians and mathematicians. The tables of contents of the volumes were finalized after extensive discussions between handbook authors and second readers. This book is intended for theoretical computer scientists; logicians.Volume Co-ordinator:: S
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Marketing Research With R And Python
This book is meant for readers with little or no experience in programming in R and Python, who wish to quickly learn what is necessary, and be able to conduct marketing research by running tests easily in R or Python.A number of marketing research textbooks have been using SPSS or SAS for many years. Conversely, R and Python can be downloaded and installed in a personal computer for free. Instructors and students do not have to go to a computer room in a university to use SPSS or SAS anymore. Instead, students can run R or Python on their personal computers.For any company, growth comes either from organic growth of existing products, or from launching successful new products. Due to competition in the marketplace, each company's marketer must determine whether or not it is time to develop and launch a new product:This book covers important frameworks and concepts of marketing research for developing a new product.
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Hands-On Network Programming with C
A comprehensive guide to programming with network sockets, implementing internet protocols, designing IoT devices, and much more with CKey FeaturesApply your C and C++ programming skills to build powerful network applicationsGet to grips with a variety of network protocols that allow you to load web pages, send emails, and do much moreWrite portable network code for Windows, Linux, and macOSBook DescriptionNetwork programming enables processes to communicate with each other over a computer network, but it is a complex task that requires programming with multiple libraries and protocols. With its support for third-party libraries and structured documentation, C is an ideal language to write network programs.Complete with step-by-step explanations of essential concepts and practical examples, this C network programming book begins with the fundamentals of Internet Protocol, TCP, and UDP. You’ll explore client-server and peer-to-peer models for information sharing and connectivity with remote computers. The book will also cover HTTP and HTTPS for communicating between your browser and website, and delve into hostname resolution with DNS, which is crucial to the functioning of the modern web. As you advance, you’ll gain insights into asynchronous socket programming and streams, and explore debugging and error handling. Finally, you’ll study network monitoring and implement security best practices.By the end of this book, you’ll have experience of working with client-server applications and be able to implement new network programs in C.The code in this book is compatible with the older C99 version as well as the latest C18 and C++17 standards. You’ll work with robust, reliable, and secure code that is portable across operating systems, including Winsock sockets for Windows and POSIX sockets for Linux and macOS.What you will learnUncover cross-platform socket programming APIsImplement techniques for supporting IPv4 and IPv6Understand how TCP and UDP connections work over IPDiscover how hostname resolution and DNS workInterface with web APIs using HTTP and HTTPSExplore Simple Mail Transfer Protocol (SMTP) for electronic mail transmissionApply network programming to the Internet of Things (IoT)Who this book is forIf you're a developer or a system administrator who wants to get started with network programming, this book is for you. Basic knowledge of C programming is assumed.
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Hands-On Web Scraping with Python
Collect and scrape different complexities of data from the modern Web using the latest tools, best practices, and techniquesKey FeaturesLearn various scraping techniques using a range of Python libraries such as Scrapy and Beautiful SoupBuild scrapers and crawlers to extract relevant information from the webAutomate web scraping operations to bridge the accuracy gap and ease complex business needsBook DescriptionWeb scraping is an essential technique used in many organizations to scrape valuable data from web pages. This book will enable you to delve deeply into web scraping techniques and methodologies. This book will introduce you to the fundamental concepts of web scraping techniques and how they can be applied to multiple sets of web pages. We'll use powerful libraries from the Python ecosystem—such as Scrapy, lxml, pyquery, bs4, and others—to carry out web scraping operations. We will take an in-depth look at essential tasks to carry out simple to intermediate scraping operations such as identifying information from web pages, using patterns or attributes to retrieve information, and others. This book adopts a practical approach to web scraping concepts and tools, guiding you through a series of use cases and showing you how to use the best tools and techniques to efficiently scrape web pages. This book also covers the use of other popular web scraping tools, such as Selenium, Regex, and web-based APIs.By the end of this book, you will have learned how to efficiently scrape the web using different techniques with Python and other popular tools.What you will learnAnalyze data and Information from web pagesLearn how to use browser-based developer tools from the scraping perspectiveUse XPath and CSS selectors to identify and explore markup elementsLearn to handle and manage cookiesExplore advanced concepts in handling HTML forms and processing loginsOptimize web securities, data storage, and API use to scrape dataUse Regex with Python to extract dataDeal with complex web entities by using Selenium to find and extract dataWho this book is forThis book is for Python programmers, data analysts, web scraping newbies, and anyone who wants to learn how to perform web scraping from scratch. If you want to begin your journey in applying web scraping techniques to a range of web pages, then this book is what you need! A working knowledge of the Python programming language is expected.
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Practical Computer Vision with SimpleCV
SimpleCV is a cross platform (Windows, Macintosh, Linux) framework in Python that makes writing computer vision applications quick and easy. This book takes you through real world examples and applications using SimpleCV so that anyone, even with zero knowledge of computer vision, can use to create their own vision application in a few minutes with a webcam and PC. SimpleCV has its own integrated shell (command line) and code editor so you can rapidly test new techniques, load and run examples, view documentation, etc. The library also works with webcams, cell phones, the Xbox Kinect, and TI's Beagle Board and Panda Board. There are 1-click installers for every platform, so you can be up and running any of the examples included in this book in a few minutes.
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Hands-On Genetic Algorithms with Python
Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPyKey FeaturesExplore the ins and outs of genetic algorithms with this fast-paced guideImplement tasks such as feature selection, search optimization, and cluster analysis using PythonSolve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applicationsBook DescriptionGenetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence.After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications.By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains.What you will learnUnderstand how to use state-of-the-art Python tools to create genetic algorithm-based applicationsUse genetic algorithms to optimize functions and solve planning and scheduling problemsEnhance the performance of machine learning models and optimize deep learning network architectureApply genetic algorithms to reinforcement learning tasks using OpenAI GymExplore how images can be reconstructed using a set of semi-transparent shapesDiscover other bio-inspired techniques, such as genetic programming and particle swarm optimizationWho this book is forThis book is for software developers, data scientists, and AI enthusiasts who want to use genetic algorithms to carry out intelligent tasks in their applications. Working knowledge of Python and basic knowledge of mathematics and computer science will help you get the most out of this book.
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