The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to take a hard pass on Knuth's brilliant but impenetrable theories and the dense multi-page proofs you'll find in most textbooks, this is the book for you. This fully-illustrated and engaging guide makes it easy for you to learn how to use algorithms effectively in your own programs.Grokking Algorithms is a disarming take on a core computer science topic. In it, you'll learn how to apply common algorithms to the practical problems you face in day-to-day life as a programmer. You'll start with problems like sorting and searching. As you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression or artificial intelligence. Whether you're writing business software, video games, mobile apps, or system utilities, you'll learn algorithmic techniques for solving problems that you thought were out of your grasp. For example, you'll be able to:Write a spell checker using graph algorithmsUnderstand how data compression works using Huffman codingIdentify problems that take too long to solve with naive algorithms, and attack them with algorithms that give you an approximate answer insteadEach carefully-presented example includes helpful diagrams and fully-annotated code samples in Python. By the end of this book, you will know some of the most widely applicable algorithms as well as how and when to use them.

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    But if one per year is good, how about Seven Languages in Seven Weeks? In this book you'll get a hands-on tour of Clojure, Haskell, Io, Prolog, Scala, Erlang, and Ruby. Whether or not your favorite language is on that list, you'll broaden your perspective of programming by examining these languages side-by-side. You'll learn something new from each, and best of all, you'll learn how to learn a language quickly. Ruby, Io, Prolog, Scala, Erlang, Clojure, Haskell. With Seven Languages in Seven Weeks, by Bruce A. Tate, you'll go beyond the syntax-and beyond the 20-minute tutorial you'll find someplace online. This book has an audacious goal: to present a meaningful exploration of seven languages within a single book. Rather than serve as a complete reference or installation guide, Seven Languages hits what's essential and unique about each language. Moreover, this approach will help teach you how to grok new languages. For each language, you'll solve a nontrivial problem, using techniques that show off the language's most important features. As the book proceeds, you'll discover the strengths and weaknesses of the languages, while dissecting the process of learning languages quickly--for example, finding the typing and programming models, decision structures, and how you interact with them. Among this group of seven, you'll explore the most critical programming models of our time. Learn the dynamic typing that makes Ruby, Python, and Perl so flexible and compelling. Understand the underlying prototype system that's at the heart of JavaScript. See how pattern matching in Prolog shaped the development of Scala and Erlang. Discover how pure functional programming in Haskell is different from the Lisp family of languages, including Clojure. Explore the concurrency techniques that are quickly becoming the backbone of a new generation of Internet applications. Find out how to use Erlang's let-it-crash philosophy for building fault-tolerant systems. Understand the actor model that drives concurrency design in Io and Scala. Learn how Clojure uses versioning to solve some of the most difficult concurrency problems. It's all here, all in one place. Use the concepts from one language to find creative solutions in another-or discover a language that may become one of your favorites.

    But developing these systems brings its own set of headaches. With lots of examples and practical advice, this book takes a holistic view of the topics that system architects and administrators must consider when building, managing, and evolving microservice architectures.Microservice technologies are moving quickly. Author Sam Newman provides you with a firm grounding in the concepts while diving into current solutions for modeling, integrating, testing, deploying, and monitoring your own autonomous services. You'll follow a fictional company throughout the book to learn how building a microservice architecture affects a single domain.Discover how microservices allow you to align your system design with your organization's goalsLearn options for integrating a service with the rest of your systemTake an incremental approach when splitting monolithic codebasesDeploy individual microservices through continuous integrationExamine the complexities of testing and monitoring distributed servicesManage security with user-to-service and service-to-service modelsUnderstand the challenges of scaling microservice architectures

    It is well balanced between theory and practice and covers topics from binary numbers and logic gates to K-maps, variable mapping, counter design etc. Each chapter includes several worked out examples to give studentsa thorough grouding in related design concepts

    The scope of the book reflects the changes that have occurred in power technology, allowing the entrance of power electronics into every facet of industrial drives, and expanding the field to open more career opportunities.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    The Productive Programmer offers critical timesaving and productivity tools that you can adopt right away, no matter what platform you use. Master developer Neal Ford not only offers advice on the mechanics of productivity-how to work smarter, spurn interruptions, get the most out your computer, and avoid repetition-he also details valuable practices that will help you elude common traps, improve your code, and become more valuable to your team. You'll learn to:Write the test before you write the codeManage the lifecycle of your objects fastidiously Build only what you need now, not what you might need later Apply ancient philosophies to software development Question authority, rather than blindly adhere to standardsMake hard things easier and impossible things possible through meta-programming Be sure all code within a method is at the same level of abstraction Pick the right editor and assemble the best tools for the job This isn't theory, but the fruits of Ford's real-world experience as an Application Architect at the global IT consultancy ThoughtWorks. Whether you're a beginner or a pro with years of experience, you'll improve your work and your career with the simple and straightforward principles in The Productive Programmer.

    The pytest testing framework helps you write tests quickly and keep them readable and maintainable - with no boilerplate code. Using a robust yet simple fixture model, it's just as easy to write small tests with pytest as it is to scale up to complex functional testing for applications, packages, and libraries. This book shows you how.For Python-based projects, pytest is the undeniable choice to test your code if you're looking for a full-featured, API-independent, flexible, and extensible testing framework. With a full-bodied fixture model that is unmatched in any other tool, the pytest framework gives you powerful features such as assert rewriting and plug-in capability - with no boilerplate code.With simple step-by-step instructions and sample code, this book gets you up to speed quickly on this easy-to-learn and robust tool. Write short, maintainable tests that elegantly express what you're testing. Add powerful testing features and still speed up test times by distributing tests across multiple processors and running tests in parallel. Use the built-in assert statements to reduce false test failures by separating setup and test failures. Test error conditions and corner cases with expected exception testing, and use one test to run many test cases with parameterized testing. Extend pytest with plugins, connect it to continuous integration systems, and use it in tandem with tox, mock, coverage, unittest, and doctest.Write simple, maintainable tests that elegantly express what you're testing and why.What You Need: The examples in this book are written using Python 3.6 and pytest 3.0. However, pytest 3.0 supports Python 2.6, 2.7, and Python 3.3-3.6.

    Written in a frank but engaging style, Debuggingprovides simple, foolproof principles guaranteed to help find any bug quickly. This book makes those shelves of application-specific debugging books (on C++, Perl, Java, etc.) obsolete. It changes the way readers think about debugging, making those pesky problems suddenly much easier to find and fix. Illustrating the rules with real-life bug-detection war stories, the book shows readers how to: * Understand the system: how perceiving the ""roadmap"" can hasten your journey * Quit thinking and look: when hands-on investigation can’t be avoided * Isolate critical factors: why changing one element at a time can be an essential tool * Keep an audit trail: how keeping a record of the debugging process can win the day

    Commercial game development expert Robert Nystrom presents an array of general solutions to problems encountered in game development. For example, you'll learn how double-buffering enables a player to perceive smooth and realistic motion, and how the service locator pattern can help you provide access to services such as sound without coupling your code to any particular sound driver or sound hardware. Games have much in common with other software, but also a number of unique constraints. Some of the patterns in this book are well-known in other domains of software development. Other of the patterns are unique to gaming. In either case, Robert Nystrom bridges from the ivory tower world of software architecture to the in-the-trenches reality of hardcore game programming. You'll learn the patterns and the general problems that they solve. You'll come away able to apply powerful and reusable architectural solutions that enable you to produce higher quality games with less effort than before. Applies classic design patterns to game programming. Introduces new patterns specific to game programming. Brings abstract software architecture down to Earth with approachable writing and an emphasis on simple code that shows each pattern in practice. What you'll learn Overcome architectural challenges unique to game programming Apply lessons from the larger software world to games. Tie different parts of a game (graphics, sound, AI) into a cohesive whole. Create elegant and maintainable architecture. Achieve good, low-level performance. Gain insight into professional, game development. Who this book is forGame Programming Patterns is aimed at professional game programmers who, while successful in shipping games, are frustrated at how hard it sometimes is to add and modify features when a game is under development. Game Programming Patterns shows how to apply modern software practices to the problem of game development while still maintaining the blazing-fast performance demanded by hard-core gamers. Game Programming Patterns also appeals to those learning about game programming in their spare time. Hobbyists and aspiring professionals alike will find much to learn in this book about pathfinding, collision detection, and other game-programming problem domains.

    You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software.This book uses the Python 3 language. The earlier Python 2 version of this book is titled "Python for Informatics: Exploring Information".

    The new edition from Chopra includes many topics encompassing the theory of structural dynamics and the application of this theory regarding earthquake analysis, response, and design of structures. No prior knowledge of structural dynamics is assumed and the manner of presentation is sufficiently detailed and integrated, to make the book suitable for self-study by students and professional engineers.

    This friendly-and-straightforward guide introduces the basics of electronics and enhances your learning experience by debunking and explaining concepts such as circuits, analog and digital, schematics, voltage, safety concerns, and more. Packed with nearly 900 pages of detailed information, this book shows you how to develop your own breadboard, design your own circuit, and get savvy with schematics. Covers the basics of electronics and demystifies a variety of electronics conceptsEncourages you to dive in and design a variety of fun and interesting entertainment electronics, mobile, and automotive projectsOffers troubleshooting advice for common electronics challengesReviews circuits, schematics, voltage, safety concerns, and much moreSo, get plugged in and start your next electronics project today with this book by your side!

    Often, computer science and computer engineering curricula don't provide students with a concentrated and consistent introduction to the fundamental concepts that underlie all computer systems. Traditional computer organization and logic design courses cover some of this material, but they focus largely on hardware design. They provide students with little or no understanding of how important software components operate, how application programs use systems, or how system attributes affect the performance and correctness of application programs. - A more complete view of systems - Takes a broader view of systems than traditional computer organization books, covering aspects of computer design, operating systems, compilers, and networking, provides students with the understanding of how programs run on real systems. - Systems presented from a programmers perspective - Material is presented in such a way that it has clear benefit to application programmers, students learn how to use this knowledge to improve program performance and reliability. They also become more effective in program debugging, because t