With this guide, pioneering Go programmer Mark Summerfield shows how to write code that takes full advantage of Go's breakthrough features and idioms. Both a tutorial and a language reference, "Programming in Go" brings together all the knowledge you need to evaluate Go, think in Go, and write high-performance software with Go. Summerfield presents multiple idiom comparisons showing exactly how Go improves upon older languages, calling special attention to Go's key innovations. Along the way, he explains everything from the absolute basics through Go's lock-free channel-based concurrency and its flexible and unusual duck-typing type-safe approach to object-orientation. Throughout, Summerfield's approach is thoroughly practical. Each chapter offers multiple live code examples designed to encourage experimentation and help you quickly develop mastery. Wherever possible, complete programs and packages are presented to provide realistic use cases, as well as exercises. Coverage includes:-- Quickly getting and installing Go, and building and running Go programs -- Exploring Go's syntax, features, and extensive standard library -- Programming Boolean values, expressions, and numeric types -- Creating, comparing, indexing, slicing, and formatting strings -- Understanding Go's highly efficient built-in collection types: slices and maps -- Using Go as a procedural programming language -- Discovering Go's unusual and flexible approach to object orientation -- Mastering Go's unique, simple, and natural approach to fine-grained concurrency -- Reading and writing binary, text, JSON, and XML files -- Importing and using standard library packages, custom packages, and third-party packages -- Creating, documenting, unit testing, and benchmarking custom packages

    You'll learn how to:Read and write files efficiently Use signals, clocks, and timers Create processes and execute programs Write secure programs Write multithreaded programs using POSIX threads Build and use shared libraries Perform interprocess communication using pipes, message queues, shared memory, and semaphores Write network applications with the sockets API While The Linux Programming Interface covers a wealth of Linux-specific features, including epoll, inotify, and the /proc file system, its emphasis on UNIX standards (POSIX.1-2001/SUSv3 and POSIX.1-2008/SUSv4) makes it equally valuable to programmers working on other UNIX platforms.The Linux Programming Interface is the most comprehensive single-volume work on the Linux and UNIX programming interface, and a book that's destined to become a new classic.Praise for The Linux Programming Interface "If I had to choose a single book to sit next to my machine when writing software for Linux, this would be it." —Martin Landers, Software Engineer, Google "This book, with its detailed descriptions and examples, contains everything you need to understand the details and nuances of the low-level programming APIs in Linux . . . no matter what the level of reader, there will be something to be learnt from this book." —Mel Gorman, Author of Understanding the Linux Virtual Memory Manager "Michael Kerrisk has not only written a great book about Linux programming and how it relates to various standards, but has also taken care that bugs he noticed got fixed and the man pages were (greatly) improved. In all three ways, he has made Linux programming easier. The in-depth treatment of topics in The Linux Programming Interface . . . makes it a must-have reference for both new and experienced Linux programmers." —Andreas Jaeger, Program Manager, openSUSE, Novell "Michael's inexhaustible determination to get his information right, and to express it clearly and concisely, has resulted in a strong reference source for programmers. While this work is targeted at Linux programmers, it will be of value to any programmer working in the UNIX/POSIX ecosystem." —David Butenhof, Author of Programming with POSIX Threads and Contributor to the POSIX and UNIX Standards ". . . a very thorough—yet easy to read—explanation of UNIX system and network programming, with an emphasis on Linux systems. It's certainly a book I'd recommend to anybody wanting to get into UNIX programming (in general) or to experienced UNIX programmers wanting to know 'what's new' in the popular GNU/Linux system." —Fernando Gont, Network Security Researcher, IETF Participant, and RFC Author ". . . encyclopedic in the breadth and depth of its coverage, and textbook-like in its wealth of worked examples and exercises. Each topic is clearly and comprehensively covered, from theory to hands-on working code. Professionals, students, educators, this is the Linux/UNIX reference that you have been waiting for." —Anthony Robins, Associate Professor of Computer Science, The University of Otago "I've been very impressed by the precision, the quality and the level of detail Michael Kerrisk put in his book. He is a great expert of Linux system calls and lets us share his knowledge and understanding of the Linux APIs." —Christophe Blaess, Author of Programmation systeme en C sous Linux ". . . an essential resource for the serious or professional Linux and UNIX systems programmer. Michael Kerrisk covers the use of all the key APIs across both the Linux and UNIX system interfaces with clear descriptions and tutorial examples and stresses the importance and benefits of following standards such as the Single UNIX Specification and POSIX 1003.1." —Andrew Josey, Director, Standards, The Open Group, and Chair of the POSIX 1003.1 Working Group "What could be better than an encyclopedic reference to the Linux system, from the standpoint of the system programmer, written by none other than the maintainer of the man pages himself? The Linux Programming Interface is comprehensive and detailed. I firmly expect it to become an indispensable addition to my programming bookshelf." —Bill Gallmeister, Author of POSIX.4 Programmer's Guide: Programming for the Real World ". . . the most complete and up-to-date book about Linux and UNIX system programming. If you're new to Linux system programming, if you're a UNIX veteran focused on portability while interested in learning the Linux way, or if you're simply looking for an excellent reference about the Linux programming interface, then Michael Kerrisk's book is definitely the companion you want on your bookshelf." —Loic Domaigne, Chief Software Architect (Embedded), Corpuls.com

    You might think the problem is your brain. It seems to have a mind of its own, a mind that doesn't always want to take in the dry, technical stuff you're forced to study. The fact is your brain craves novelty. It's constantly searching, scanning, waiting for something unusual to happen. After all, that's the way it was built to help you stay alive. It takes all the routine, ordinary, dull stuff and filters it to the background so it won't interfere with your brain's real work--recording things that matter. How does your brain know what matters? It's like the creators of the Head First approach say, suppose you're out for a hike and a tiger jumps in front of you, what happens in your brain? Neurons fire. Emotions crank up. Chemicals surge. That's how your brain knows.And that's how your brain will learn Java. Head First Java combines puzzles, strong visuals, mysteries, and soul-searching interviews with famous Java objects to engage you in many different ways. It's fast, it's fun, and it's effective. And, despite its playful appearance, Head First Java is serious stuff: a complete introduction to object-oriented programming and Java. You'll learn everything from the fundamentals to advanced topics, including threads, network sockets, and distributed programming with RMI. And the new. second edition focuses on Java 5.0, the latest version of the Java language and development platform. Because Java 5.0 is a major update to the platform, with deep, code-level changes, even more careful study and implementation is required. So learning the Head First way is more important than ever. If you've read a Head First book, you know what to expect--a visually rich format designed for the way your brain works. If you haven't, you're in for a treat. You'll see why people say it's unlike any other Java book you've ever read.By exploiting how your brain works, Head First Java compresses the time it takes to learn and retain--complex information. Its unique approach not only shows you what you need to know about Java syntax, it teaches you to think like a Java programmer. If you want to be bored, buy some other book. But if you want to understand Java, this book's for you.

    It is suitable for a one or two semester university course on AI, as well as for researchers in the field.

    This introduction is ideal for beginners: it requires no previous programming experience and all concepts are explained from first principles via carefully chosen examples. Each chapter includes exercises that range from the straightforward to extended projects, plus suggestions for further reading on more advanced topics. The author is a leading Haskell researcher and instructor, well-known for his teaching skills. The presentation is clear and simple, and benefits from having been refined and class-tested over several years. The result is a text that can be used with courses, or for self-learning. Features include freely accessible Powerpoint slides for each chapter, solutions to exercises and examination questions (with solutions) available to instructors, and a downloadable code that's fully compliant with the latest Haskell release.

    Introducing students to the many facets of neural networks, this text provides many case studies to illustrate their real-life, practical applications.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    Like previous versions, this guide shows you step-by-step techniques on using MVC to best advantage, with plenty of practical tutorials to illustrate the concepts. It covers controllers, views, and models; forms and HTML helpers; data annotation and validation; membership, authorization, and security.MVC 5, the latest version of MVC, adds sophisticated features such as single page applications, mobile optimization, and adaptive rendering A team of top Microsoft MVP experts, along with visionaries in the field, provide practical advice on basic and advanced MVC topics Covers controllers, views, models, forms, data annotations, authorization and security, Ajax, routing, ASP.NET web API, dependency injection, unit testing, real-world application, and much more Professional ASP.NET MVC 5 is the comprehensive resource you need to make the best use of the updated Model-View-Controller technology.

    The book comprises chapters on elementary data structures, dynamic programming, backtracking, algebraic problems, lower bound theory, pram algorithms, mesh algorithms, and hypercube algorithms. In addition, the book consists of several real-world examples to understand the concepts better. This book is indispensable for computer engineers preparing for competitive examinations like GATE and IES.

    And this book will teach you all you need to know to be immediately productive with MySQL. By working through 30 highly focused hands-on lessons, your MySQL Crash Course will be both easier and more effective than you'd have thought possible. Learn how to: Retrieve and sort data Filter data using comparisons, regular expressions, full text search, and much more Join relational data Create and alter tables Insert, update, and delete data Leverage the power of stored procedures and triggers Use views and Cursors Manage transactional processing Create user accounts and manage security via access control Ben Forta is Macromedia's Senior Technical Evangelist, and has almost 20 years of experience in the computer industry in product development, support, training, and product marketing. Ben is the author of the best-selling Sams Teach Yourself SQL in 10 Minutes (now in its third edition, and translated into over a dozen languages), ColdFusion Web Application Construction Kit, and Advanced ColdFusion Development (both published by Que Publishing), Sams Teach Yourself Regular Expressions in 10 Minutes, as well as books on SQL, Flash, JSP, HomeSite, WAP, Windows 2000, and other subjects.

     Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    Vaughn Vernon couples guided approaches to implementation with modern architectures, highlighting the importance and value of focusing on the business domain while balancing technical considerations.Building on Eric Evans’ seminal book, Domain-Driven Design, the author presents practical DDD techniques through examples from familiar domains. Each principle is backed up by realistic Java examples–all applicable to C# developers–and all content is tied together by a single case study: the delivery of a large-scale Scrum-based SaaS system for a multitenant environment.The author takes you far beyond “DDD-lite” approaches that embrace DDD solely as a technical toolset, and shows you how to fully leverage DDD’s “strategic design patterns” using Bounded Context, Context Maps, and the Ubiquitous Language. Using these techniques and examples, you can reduce time to market and improve quality, as you build software that is more flexible, more scalable, and more tightly aligned to business goals.