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.

    You could spend years discovering solutions to them all, step by step as you encounter them. Or you can just look in here. All successful database applications are destined to eventually run into issues scaling up their performance. Peek into the future of your PostgreSQL database's problems today. Know the warning signs to look for, and how to avoid the most common issues before they even happen. Surprisingly, most PostgreSQL database applications evolve in the same way: Choose the right hardware. Tune the operating system and server memory use. Optimize queries against the database, with the right indexes. Monitor every layer, from hardware to queries, using some tools that are inside PostgreSQL and others that are external. Using monitoring insight, continuously rework the design and configuration. On reaching the limits of a single server, break things up; connection pooling, caching, partitioning, and replication can all help handle increasing database workloads. The path to a high performance database system isn't always easy. But it doesn't have to be mysterious with the right guide. This book is a clear, step-by-step guide to optimizing and scaling up PostgreSQL database servers. - Publisher.

    It's an essential read for anyone dealing with scaling an online business."--Chris Lalonde, VP, Technical Operations and Infrastructure Architecture, Bullhorn "Abbott and Fisher again tackle the difficult problem of scalability in their unique and practical manner. Distilling the challenges of operating a fast-growing presence on the Internet into 50 easy-to understand rules, the authors provide a modern cookbook of scalability recipes that guide the reader through the difficulties of fast growth."--Geoffrey Weber, Vice President, Internet Operations, Shutterfly "Abbott and Fisher have distilled years of wisdom into a set of cogent principles to avoid many nonobvious mistakes."--Jonathan Heiliger, VP, Technical Operations, Facebook "In "The Art of Scalability," the AKF team taught us that scale is not just a technology challenge. Scale is obtained only through a combination of people, process, "and "technology. With "Scalability Rules," Martin Abbott and Michael Fisher fill our scalability toolbox with easily implemented and time-tested rules that once applied will enable massive scale."--Jerome Labat, VP, Product Development IT, Intuit "When I joined Etsy, I partnered with Mike and Marty to hit the ground running in my new role, and it was one of the best investments of time I have made in my career. The indispensable advice from my experience working with Mike and Marty is fully captured here in this book. Whether you're taking on a role as a technology leader in a new company or you simply want to make great technology decisions, "Scalability Rules "will be the go-to resource on your bookshelf."--Chad Dickerson, CTO, Etsy ""Scalability Rules "provides an essential set of practical tools and concepts anyone can use when designing, upgrading, or inheriting a technology platform. It's very easy to focus on an immediate problem and overlook issues that will appear in the future. This book ensures strategic design principles are applied to everyday challenges."--Robert Guild, Director and Senior Architect, Financial Services "An insightful, practical guide to designing and building scalable systems. A must-read for both product-building and operations teams, this book offers concise and crisp insights gained from years of practical experience of AKF principals. With the complexity of modern systems, scalability considerations should be an integral part of the architecture and implementation process. Scaling systems for hypergrowth requires an agile, iterative approach that is closely aligned with product features; this book shows you how."--Nanda Kishore, Chief Technology Officer, ShareThis "For organizations looking to scale technology, people, and processes rapidly or effectively, the twin pairing of "Scalability Rules "and "The Art of Scalability "are unbeatable. The rules-driven approach in "Scalability Rules "makes this not only an easy reference companion, but also allows organizations to tailor the Abbott and Fisher approach to their specific needs both immediately and in the future!"--Jeremy Wright, CEO, BNOTIONS.ca and Founder, b5media 50 Powerful, Easy-to-Use Rules for Supporting Hypergrowth in Any Environment "Scalability Rules" is the easy-to-use scalability primer and reference for every architect, developer, web professional, and manager. Authors Martin L. Abbott and Michael T. Fisher have helped scale more than 200 hypergrowth Internet sites through their consulting practice. Now, drawing on their unsurpassed experience, they present 50 clear, proven scalability rules-and practical guidance for applying them. Abbott and Fisher transform scalability from a "black art" to a set of realistic, technology-agnostic best practices for supporting hypergrowth in nearly any environment, including both frontend and backend systems. For architects, they offer powerful new insights for creating and evaluating designs. For developers, they share specific techniques for handling everything from databases to state. For managers, they provide invaluable help in goal-setting, decision-making, and interacting with technical teams. Whatever your role, you'll find practical risk/benefit guidance for setting priorities-and getting maximum "bang for the buck." - Simplifying architectures and avoiding "over-engineering"- Scaling via cloning, replication, separating functionality, and splitting data sets- Scaling out, not up- Getting more out of databases without compromising scalability- Avoiding unnecessary redirects and redundant double-checking- Using caches and content delivery networks more aggressively, without introducing unacceptable complexity- Designing for fault tolerance, graceful failure, and easy rollback- Striving for statelessness when you can; efficiently handling state when you must- Effectively utilizing asynchronous communication- Learning quickly from mistakes, and much more

    The cause of their failure was a complete mystery.Five months later, a seemingly unrelated event occurred. A computer security firm in Belarus was called in to troubleshoot some computers in Iran that were caught in a reboot loop—crashing and rebooting repeatedly. At first, technicians with the firm believed the malicious code they found on the machines was a simple, routine piece of malware. But as they and other experts around the world investigated, they discovered a virus of unparalleled complexity and mysterious provenance and intent. They had, they soon learned, stumbled upon the world’s first digital weapon.Stuxnet, as it came to be known, was unlike any other virus or worm built before: It was the first attack that reached beyond the computers it targeted to physically destroy the equipment those computers controlled. It was an ingenious attack, jointly engineered by the United States and Israel, that worked exactly as planned, until the rebooting machines gave it all away. And the discovery of Stuxnet was just the beginning: Once the digital weapon was uncovered and deciphered, it provided clues to other tools lurking in the wild. Soon, security experts found and exposed not one but three highly sophisticated digital spy tools that came from the same labs that created Stuxnet. The discoveries gave the world its first look at the scope and sophistication of nation-state surveillance and warfare in the digital age.Kim Zetter, a senior reporter at Wired, has covered hackers and computer security since 1999 and is one of the top journalists in the world on this beat. She was among the first reporters to cover Stuxnet after its discovery and has authored many of the most comprehensive articles about it. In COUNTDOWN TO ZERO DAY: Stuxnet and the Launch of the World’s First Digital Weapon, Zetter expands on this work to show how the code was designed and unleashed and how its use opened a Pandora’s Box, ushering in an age of digital warfare in which any country’s infrastructure—power grids, nuclear plants, oil pipelines, dams—is vulnerable to the same kind of attack with potentially devastating results. A sophisticated digital strike on portions of the power grid, for example, could plunge half the U.S. into darkness for weeks or longer, having a domino effect on all other critical infrastructures dependent on electricity.

    Written for both experienced and new programmers alike, Learning Ruby is a just-get-in-and-drive book -- a hands-on tutorial that offers lots of Ruby programs and lets you know how and why they work, just enough to get you rolling down the road. Interest in Ruby stems from the popularity of Rails, the web development framework that's attracting new devotees and refugees from Java and PHP. But there are plenty of other uses for this versatile language. The best way to learn is to just try the code! You'll find examples on nearly every page of this book that you can imitate and hack. Briefly, this book:Outlines many of the most important features of Ruby Demonstrates how to use conditionals, and how to manipulate strings in Ruby. Includes a section on regular expressions Describes how to use operators, basic math, functions from the Math module, rational numbers, etc. Talks you through Ruby arrays, and demonstrates hashes in detail Explains how to process files with Ruby Discusses Ruby classes and modules (mixins) in detail, including a brief introduction to object-oriented programming (OOP) Introduces processing XML, the Tk toolkit, RubyGems, reflection, RDoc, embedded Ruby, metaprogramming, exception handling, and other topics Acquaints you with some of the essentials of Rails, and includes a short Rails tutorial. Each chapter concludes with a set of review questions, and appendices provide you with a glossary of terms related to Ruby programming, plus reference material from the book in one convenient location. If you want to take Ruby out for a drive, Learning Ruby holds the keys.

    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.

    No longer is execution speed the sole criterion for judging compiled code. Today, code might be judged on how small it is, how much power it consumes, how well it compresses, or how many page faults it generates. In this evolving environment, the task of building a successful compiler relies upon the compiler writer's ability to balance and blend algorithms, engineering insights, and careful planning. Today's compiler writer must choose a path through a design space that is filled with diverse alternatives, each with distinct costs, advantages, and complexities.Engineering a Compiler explores this design space by presenting some of the ways these problems have been solved, and the constraints that made each of those solutions attractive. By understanding the parameters of the problem and their impact on compiler design, the authors hope to convey both the depth of the problems and the breadth of possible solutions. Their goal is to cover a broad enough selection of material to show readers that real tradeoffs exist, and that the impact of those choices can be both subtle and far-reaching.Authors Keith Cooper and Linda Torczon convey both the art and the science of compiler construction and show best practice algorithms for the major passes of a compiler. Their text re-balances the curriculum for an introductory course in compiler construction to reflect the issues that arise in current practice.

    

    Algorithms in a Nutshell describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the right algorithm for your needs -- with just enough math to let you understand and analyze algorithm performance. With its focus on application, rather than theory, this book provides efficient code solutions in several programming languages that you can easily adapt to a specific project. Each major algorithm is presented in the style of a design pattern that includes information to help you understand why and when the algorithm is appropriate. With this book, you will:Solve a particular coding problem or improve on the performance of an existing solutionQuickly locate algorithms that relate to the problems you want to solve, and determine why a particular algorithm is the right one to useGet algorithmic solutions in C, C++, Java, and Ruby with implementation tipsLearn the expected performance of an algorithm, and the conditions it needs to perform at its bestDiscover the impact that similar design decisions have on different algorithmsLearn advanced data structures to improve the efficiency of algorithmsWith Algorithms in a Nutshell, you'll learn how to improve the performance of key algorithms essential for the success of your software applications.

    Database in Depth: The Relational Model for Practitioners goes beyond the hype and gets to the heart of how relational databases actually work.Ideal for experienced database developers and designers, this concise guide gives you a clear view of the technology--a view that's not influenced by any vendor or product. Featuring an extensive set of exercises, it will help you:understand why and how the relational model is still directly relevant to modern database technology (and will remain so for the foreseeable future)see why and how the SQL standard is seriously deficientuse the best current theoretical knowledge in the design of their databases and database applicationsmake informed decisions in their daily database professional activitiesDatabase in Depth will appeal not only to database developers and designers, but also to a diverse field of professionals and academics, including database administrators (DBAs), information modelers, database consultants, and more. Virtually everyone who deals with relational databases should have at least a passing understanding of the fundamentals of working with relational models.Author C.J. Date has been involved with the relational model from its earliest days. An exceptionally clear-thinking writer, Date lays out principle and theory in a manner that is easily understood. Few others can speak as authoritatively the topic of relational databases as Date can.

    This book teaches you how to conduct examinations by explaining what digital forensics is, the methodologies used, key technical concepts and the tools needed to perform examinations. Details on digital forensics for computers, networks, cell phones, GPS, the cloud, and Internet are discussed. Readers will also learn how to collect evidence, document the scene, and recover deleted data. This is the only resource your students need to get a jump-start into digital forensics investigations.This book is organized into 11 chapters. After an introduction to the basics of digital forensics, the book proceeds with a discussion of key technical concepts. Succeeding chapters cover labs and tools; collecting evidence; Windows system artifacts; anti-forensics; Internet and email; network forensics; and mobile device forensics. The book concludes by outlining challenges and concerns associated with digital forensics. PowerPoint lecture slides are also available.This book will be a valuable resource for entry-level digital forensics professionals as well as those in complimentary fields including law enforcement, legal, and general information security.

    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.

    Restructured into six parts, this new edition covers a wide spectrum of software processes from initial requirements solicitation through design and development.

    KEY TOPICS: Written in a reader-friendly style, the book encourages broad problem-solving skills while thoroughly covering the material required for introductory algorithms. The author emphasizes conceptual understanding before the introduction of the formal treatment of each technique. Popular puzzles are used to motivate readers' interest and strengthen their skills in algorithmic problem solving. Other enhancement features include chapter summaries, hints to the exercises, and a solution manual. MARKET: For those interested in learning more about algorithms.

    We have put thousands of hours into the fourth edition of the book, writing and revising its material to include significant improvements and new material based on feedback from previous editions.