But while most of us today can work a computer--albeit with the help of the ever-present computer software manual--we know little about what goes on inside the box and virtually nothing about software designor the world of computer programming. In Patterns of Software, the respected software pioneer and computer scientist, Richard Gabriel, gives us an informative inside look at the world of software design and computer programming and the business that surrounds them. In this wide-ranging volume, Gabriel discusses such topics as whatmakes a successful programming language, how the rest of the world looks at and responds to the work of computer scientists, how he first became involved in computer programming and software development, what makes a successful software business, and why his own company, Lucid, failed in 1994, tenyears after its inception. Perhaps the most interesting and enlightening section of the book is Gabriel's detailed look at what he believes are the lessons that can be learned from architect Christopher Alexander, whose books--including the seminal A Pattern Language--have had a profound influence on the computer programmingcommunity. Gabriel illuminates some of Alexander's key insights--the quality without a name, pattern languages, habitability, piecemeal growth--and reveals how these influential architectural ideas apply equally well to the construction of a computer program. Gabriel explains the concept ofhabitability, for example, by comparing a program to a New England farmhouse and the surrounding structures which slowly grow and are modified according to the needs and desires of the people who live and work on the farm. Programs live and grow, and their inhabitants--the programmers--need to workwith that program the way the farmer works with the homestead. Although computer scientists and software entrepreneurs will get much out of this book, the essays are accessible to everyone and will intrigue anyone curious about Silicon Valley, computer programming, or the world of high technology.

     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.

    Distinguishing agents (e.g., molecules, cells, animals, and species) from their interactions (e.g., chemical reactions, immune system responses, sexual reproduction, and evolution), Flake argues that it is the computational properties of interactions that account for much of what we think of as beautiful and interesting. From this basic thesis, Flake explores what he considers to be today's four most interesting computational topics: fractals, chaos, complex systems, and adaptation.Each of the book's parts can be read independently, enabling even the casual reader to understand and work with the basic equations and programs. Yet the parts are bound together by the theme of the computer as a laboratory and a metaphor for understanding the universe. The inspired reader will experiment further with the ideas presented to create fractal landscapes, chaotic systems, artificial life forms, genetic algorithms, and artificial neural networks.

    Now, in this lucid and accessibly written book, Philip Ball applies state-of-the-art scientific understanding from the fields of biology, chemistry, geology, physics, and mathematics to these ancient mysteries, revealing how nature's seemingly complex patterns originate in simple physical laws. Tracing the history of scientific thought about natural patterns, Ball shows how common presumptions--for example, that complex form must be guided by some intelligence or that form always follows function--are erroneous and continue to mislead scientists today. He investigates specific patterns in depth, revealing that these designs are self-organized and that simple, local interactions between component parts produce motifs like spots, stripes, branches, and honeycombs. In the process, he examines the mysterious phenomenon of symmetry and why it appears--and breaks--in similar ways in different systems. Finally, he attempts to answer this profound question: why are some patterns universal? Illustrations throughout the text, many in full color, beautifully illuminate Ball's ideas.

    With this book you will learn how to tap into every Kindle Fire feature, including many of the hidden ones not discussed in other books. From setting up your Kindle Page, managing your music, watching movies, and downloading content - this book covers everything. The task based full-color format allows you to quickly and easily find the exact task you want to accomplish and walks you through it in a delightfully concise and visual manner. My Kindle Fire makes it easy for you to:• Master all the basics, fast: reading, playing, watching, browsing, and more• Tweak your Kindle Fire for quicker access and longer battery life• Sample best-seller book chapters for free• Mark up any eBook with highlights, notes, and bookmarks• Convert your personal documents for use on Kindle Fire• Discover Calibre, a powerful eBook management tool• Control even the largest music libraries• Get instant answers from Wikipedia, and from Kindle Fire’s built-in dictionary• Listen to personalized Internet radio stations created just for you• Use your Kindle Fire as a digital photo frame• Prevent unwanted subscription charges• Set up any email account to work on your Kindle Fire• Explore any web content with Amazon’s innovative Silk browser• Use Amazon Cloud to get your stuff anywhere—even if you left your Kindle at home• And much much more…Unlike many other guides which might only briefly mention or skip over some very import Kindle Fire features My Kindle Fire covers everything. Here are just a few of the things you'll find in My Kindle Fire that aren't covered in other guides: • Full coverage of Calibre, a free application for Mac or PC that helps manage your eBook library• A large number of walkthroughs for managing music playlists, including how to use cloud playlists. • How to reinstall multiple apps at once in case you reset your Kindle Fire. • Step-by-step walkthroughs on configuring all types of email accounts. • Coverage on handling attachments in your email application. • Importing contacts from your existing email application or cloud service and how you can export your contacts in order to back them up. • How to use Copy and Paste on the Kindle Fire. • How to use social networking integration with Facebook and others. • Walkthroughs on using the Gallery app, Pandora, Audible, and other popular apps. • Shows you how to access files on other computers in your house right from your Kindle Fire.

    In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect.Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change. Today, automated systems—rather than humans—control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor.In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile.The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values.This deeply researched and passionate book could not be more timely.Naomi Klein: "This book is downright scary."Ethan Zuckerman, MIT: "Should be required reading."Dorothy Roberts, author of Killing the Black Body: "A must-read for everyone concerned about modern tools of inequality in America."Astra Taylor, author of The People's Platform: "This is the single most important book about technology you will read this year."

    With no prior knowledge of game development or 3D required, you will learn from scratch, taking each concept at a time working up to a full 3D mini-game. You'll learn scripting with C# or JavaScript and master the Unity development environment with easy-to-follow stepwise tasks. If you're a designer or animator who wishes to take their first steps into game development or prototyping, or if you've simply spent many hours sitting in front of video games, with ideas bubbling away in the back of your mind, Unity and this book should be your starting point. No prior knowledge of game production is required, inviting you to simply bring with you a passion for making great games.

    With a focus on core concepts and best practices rather than on individual languages, platforms, or technologies, Web Scalability for Startup Engineers describes how infrastructure and software architecture work together to support a scalable environment.You'll learn, step by step, how scalable systems work and how to solve common challenges. Helpful diagrams are included throughout, and real-world examples illustrate the concepts presented. Even if you have limited time and resources, you can successfully develop and deliver robust, scalable web applications with help from this practical guide.Learn the key principles of good software design required for scalable systemsBuild the front-end layer to sustain the highest levels of concurrency and request ratesDesign and develop web services, including REST-ful APIsEnable a horizontally scalable data layerImplement caching best practicesLeverage asynchronous processing, messaging, and event-driven architectureStructure, index, and store data for optimized searchExplore other aspects of scalability, such as automation, project management, and agile teams

    Xamarin.Forms lets you write shared user-interface code in C# and XAML that maps to native controls on these three platforms.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    Publilius Syrus, Moral Sayings We've been very fortunate to receive fantastic feedback from our readers during the last four years, since the first edition of How to Solve It: Modern Heuristics was published in 1999. It's heartening to know that so many people appreciated the book and, even more importantly, were using the book to help them solve their problems. One professor, who published a review of the book, said that his students had given the best course reviews he'd seen in 15 years when using our text. There can be hardly any better praise, except to add that one of the book reviews published in a SIAM journal received the best review award as well. We greatly appreciate your kind words and personal comments that you sent, including the few cases where you found some typographical or other errors. Thank you all for this wonderful support.

    However, databases can be complex. That's why you need the expert guidance in this comprehensive reference. Access 2013 Bible helps you gain a solid understanding of database purpose, construction, and application so that whether you're new to Access or looking to upgrade to the 2013 version, this well-rounded resource provides you with a thorough look at everything Access can do.Explains how to create tables, manipulate datasheets, and work with multiple tables Teaches you how to apply the seven-step design method to build databases that are tailored to your needs Covers building forms with wizards, creating bound and unbound forms, and adding data validation Shows you ways to automate query parameters, create functions and subroutines, and add programmed error routines Features a bonus website with content that contains all source code from the book as well as bonus shareware, freeware, trial, demo, and evaluation programs If you are looking for a comprehensive book on all things Access, look no further than Access 2013 Bible.

    Based on the authors' intensive summer C++ courses at Stanford University, Accelerated C++ covers virtually every concept that most professional C++ programmers will ever use -- but it turns the traditional C++ curriculum upside down, starting with the high-level C++ data structures and algorithms that let you write robust programs immediately. Once you're getting results, Accelerated C++ takes you under the hood, introducing complex language features such as memory management in context, and explaining exactly how and when to use them. From start to finish, the book concentrates on solving problems, rather than learning language and library features for their own sake. The result: You'll be writing real-world programs in no time -- and outstanding code faster than you ever imagined.

    With both explanation and demonstration, Davey guides you step-by-step through the entire process of generating and validating an idea, setting entry and exit points, testing systems, and implementing them in live trading. You'll find concrete rules for increasing or decreasing allocation to a system, and rules for when to abandon one. The companion website includes Davey's own Monte Carlo simulator and other tools that will enable you to automate and test your own trading ideas.A purely discretionary approach to trading generally breaks down over the long haul. With market data and statistics easily available, traders are increasingly opting to employ an automated or algorithmic trading system—enough that algorithmic trades now account for the bulk of stock trading volume. Building Algorithmic Trading Systems teaches you how to develop your own systems with an eye toward market fluctuations and the impermanence of even the most effective algorithm. Learn the systems that generated triple-digit returns in the World Cup Trading Championship Develop an algorithmic approach for any trading idea using off-the-shelf software or popular platforms Test your new system using historical and current market data Mine market data for statistical tendencies that may form the basis of a new system Market patterns change, and so do system results. Past performance isn't a guarantee of future success, so the key is to continually develop new systems and adjust established systems in response to evolving statistical tendencies. For individual traders looking for the next leap forward, Building Algorithmic Trading Systems provides expert guidance and practical advice.