Bowman, the author of the best-selling Ten-Minute Trainer, comes the dynamic new book, Training from the BACK of the Room! This innovative resource introduces 65 training strategies that are guaranteed to deliver outstanding training results no matter what the topic, group, or learning environment. Now, trainers can replace the traditional Trainers talk; learners listen paradigm with a radical new model for designing and delivering instruction: When learners talk and teach, they learn.

    It requires three to five times less time than developing in Java, is a great building block for learning both procedural and object-oriented programming concepts, and is an ideal language for data analysis. Beginning Programming with Python For Dummies is the perfect guide to this dynamic and powerful programming language--even if you've never coded before! Author John Paul Mueller draws on his vast programming knowledge and experience to guide you step-by-step through the syntax and logic of programming with Python and provides several real-world programming examples to give you hands-on experience trying out what you've learned.Provides a solid understanding of basic computer programming concepts and helps familiarize you with syntax and logic Explains the fundamentals of procedural and object-oriented programming Shows how Python is being used for data analysis and other applications Includes short, practical programming samples to apply your skills to real-world programming scenarios Whether you've never written a line of code or are just trying to pick up Python, there's nothing to fear with the fun and friendly Beginning Programming with Python For Dummies leading the way.

    HOW RISKY IS IT, REALLY?International risk expert David Ropeik takes an in-depth look at our perceptions of risk and explains the hidden factors that make us unnecessarily afraid of relatively small threats and not afraid enough of some really big ones. This read is a comprehensive, accessible, and entertaining mixture of what's been discovered about how and why we fear — too much or too little. It brings into focus the danger of The Perception Gap: when our fears don't match the facts, and we make choices that create additional risks.This book will not decide for you what is really risky and what isn't. That's up to you. HOW RISKY IS IT, REALLY? will tell you how you make those decisions. Understanding how we perceive risk is the first step toward making wiser and healthier choices for ourselves as individuals and for society as a whole.TEST YOUR OWN "RISK RESPONSE" IN DOZENS OF SELF-QUIZZES!

    These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

    They aren't conscious of all the choices they make, like how they format their source, the names they use for variables, or the kinds of loops they use. They're focused entirely on problems they're solving, solutions they're creating, and algorithms they're implementing. So they write code in the way that seems natural, that happens intuitively, and that feels good.But if you're serious about your profession, intuition isn't enough. Perl Best Practices author Damian Conway explains that rules, conventions, standards, and practices not only help programmers communicate and coordinate with one another, they also provide a reliable framework for thinking about problems, and a common language for expressing solutions. This is especially critical in Perl, because the language is designed to offer many ways to accomplish the same task, and consequently it supports many incompatible dialects.With a good dose of Aussie humor, Dr. Conway (familiar to many in the Perl community) offers 256 guidelines on the art of coding to help you write better Perl code--in fact, the best Perl code you possibly can. The guidelines cover code layout, naming conventions, choice of data and control structures, program decomposition, interface design and implementation, modularity, object orientation, error handling, testing, and debugging.They're designed to work together to produce code that is clear, robust, efficient, maintainable, and concise, but Dr. Conway doesn't pretend that this is the one true universal and unequivocal set of best practices. Instead, Perl Best Practices offers coherent and widely applicable suggestions based on real-world experience of how code is actually written, rather than on someone's ivory-tower theories on how software ought to be created.Most of all, Perl Best Practices offers guidelines that actually work, and that many developers around the world are already using. Much like Perl itself, these guidelines are about helping you to get your job done, without getting in the way.Praise for Perl Best Practices from Perl community members:"As a manager of a large Perl project, I'd ensure that every member of my team has a copy of Perl Best Practices on their desk, and use it as the basis for an in-house style guide." -- Randal Schwartz"There are no more excuses for writing bad Perl programs. All levels of Perl programmer will be more productive after reading this book." -- Peter Scott"Perl Best Practices will be the next big important book in the evolution of Perl. The ideas and practices Damian lays down will help bring Perl out from under the embarrassing heading of "scripting languages". Many of us have known Perl is a real programming language, worthy of all the tasks normally delegated to Java and C++. With Perl Best Practices, Damian shows specifically how and why, so everyone else can see, too." -- Andy Lester"Damian's done what many thought impossible: show how to build large, maintainable Perl applications, while still letting Perl be the powerful, expressive language that programmers have loved for years." -- Bill Odom"Finally, a means to bring lasting order to the process and product of real Perl development teams." -- Andrew Sundstrom"Perl Best Practices provides a valuable education in how to write robust, maintainable P

    Why? Too many organizations still view software development as just another production line. Too many developers feel that way, too--and they behave accordingly. In The Software Craftsman: Professionalism, Pragmatism, Pride, Sandro Mancuso offers a better and more fulfilling path. If you want to develop software with pride and professionalism; love what you do and do it with excellence; and build a career with autonomy, mastery, and purpose, it starts with the recognition that you are a craftsman. Once you embrace this powerful mindset, you can achieve unprecedented levels of technical excellence and customer satisfaction. Mancuso helped found the world's largest organization of software craftsmen; now, he shares what he's learned through inspiring examples and pragmatic advice you can use in your company, your projects, and your career. You will learn Why agile processes aren't enough and why craftsmanship is crucial to making them work How craftsmanship helps you build software right and helps clients in ways that go beyond code How and when to say "No" and how to provide creative alternatives when you do Why bad code happens to good developers and how to stop creating and justifying it How to make working with legacy code less painful and more productive How to be pragmatic--not dogmatic--about your practices and tools How to lead software craftsmen and attract them to your organization What to avoid when advertising positions, interviewing candidates, and hiring developers How developers and their managers can create a true culture of learning How to drive true technical change and overcome deep patterns of skepticism Sandro Mancuso has coded for startups, software houses, product companies, international consultancies, and investment banks. In October 2013, he cofounded Codurance, a consultancy based on Software Craftsmanship principles and values. His involvement with Software Craftsmanship began in 2010, when he founded the London Software Craftsmanship Community (LSCC), now the world's largest and most active Software Craftsmanship community, with more than two thousand craftsmen. For the past four years, he has inspired and helped developers to organize Software Craftsmanship communities throughout Europe, the United States, and the rest of the world.

    But building a machine learning system requires that you make practical decisions: Should you collect more training data? Should you use end-to-end deep learning? How do you deal with your training set not matching your test set? and many more. Historically, the only way to learn how to make these "strategy" decisions has been a multi-year apprenticeship in a graduate program or company. This is a book to help you quickly gain this skill, so that you can become better at building AI systems.

    With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples.

    The book details the major subsystems and features of the Linux kernel, including its design, implementation, and interfaces. It covers the Linux kernel with both a practical and theoretical eye, which should appeal to readers with a variety of interests and needs. The author, a core kernel developer, shares valuable knowledge and experience on the 2.6 Linux kernel. Specific topics covered include process management, scheduling, time management and timers, the system call interface, memory addressing, memory management, the page cache, the VFS, kernel synchronization, portability concerns, and debugging techniques. This book covers the most interesting features of the Linux 2.6 kernel, including the CFS scheduler, preemptive kernel, block I/O layer, and I/O schedulers. The third edition of Linux Kernel Development includes new and updated material throughout the book:An all-new chapter on kernel data structuresDetails on interrupt handlers and bottom halvesExtended coverage of virtual memory and memory allocationTips on debugging the Linux kernelIn-depth coverage of kernel synchronization and lockingUseful insight into submitting kernel patches and working with the Linux kernel community

    But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart.Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now.Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people.

     They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own machine learning algorithms in Python, this book is for you.

    This long-awaited revision contains changes throughout the text. There are new implementations of most of the major programming systems in the book, including the interpreters and compilers, and the authors have incorporated many small changes that reflect their experience teaching the course at MIT since the first edition was published. A new theme has been introduced that emphasizes the central role played by different approaches to dealing with time in computational models: objects with state, concurrent programming, functional programming and lazy evaluation, and nondeterministic programming. There are new example sections on higher-order procedures in graphics and on applications of stream processing in numerical programming, and many new exercises. In addition, all the programs have been reworked to run in any Scheme implementation that adheres to the IEEE standard.

    You know you need concurrent applications, but also know these are almost impossible to get right. Meet Elixir, a functional, concurrent language built on the rock-solid Erlang VM. Elixir’s pragmatic syntax and built-in support for metaprogramming will make you productive and keep you interested for the long haul. This book is the introduction to Elixir for experienced programmers.Maybe you need something that’s closer to Ruby, but with a battle-proven environment that’s unrivaled for massive scalability, concurrency, distribution, and fault tolerance. Maybe the time is right for the Next Big Thing. Maybe it’s Elixir.

    And how to move all of this data becomes nearly as important as the data itself. If you� re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds.Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you� ll learn Kafka� s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.Understand publish-subscribe messaging and how it fits in the big data ecosystem.Explore Kafka producers and consumers for writing and reading messagesUnderstand Kafka patterns and use-case requirements to ensure reliable data deliveryGet best practices for building data pipelines and applications with KafkaManage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasksLearn the most critical metrics among Kafka� s operational measurementsExplore how Kafka� s stream delivery capabilities make it a perfect source for stream processing systems

    Should you wish to adopt a framework or employ a processor to speed up your development process, be our guest. However, this book is not about you; it’s about your audience.The Inclusive Design Patterns book covers all the techniques, gotchas and strategies you need to be aware of when building accessible, inclusive interfaces. We’ll explore the document outline, external links and “skip” links, navigation regions and landmarks, labelling and alternative text for illustrations, buttons, tables of contents, JavaScript patterns, touch targets, filter widgets and infinite scrolling and “load more” button and grid display and dynamic content and tab interfaces and password validation and web forms and error messages — and pretty much anything else you need to know about accessibility, including how to prototype with inclusivity in mind, how to deal with legacy browsers and dozens of practical snippets to use when building inclusive interfaces.