But what is it physically? And where is it really? Our mental map of the network is as blank as the map of the ocean that Columbus carried on his first Atlantic voyage. The Internet, its material nuts and bolts, is an unexplored territory. Until now.In Tubes, journalist Andrew Blum goes inside the Internet's physical infrastructure and flips on the lights, revealing an utterly fresh look at the online world we think we know. It is a shockingly tactile realm of unmarked compounds, populated by a special caste of engineer who pieces together our networks by hand; where glass fibers pulse with light and creaky telegraph buildings, tortuously rewired, become communication hubs once again. From the room in Los Angeles where the Internet first flickered to life to the caverns beneath Manhattan where new fiber-optic cable is buried; from the coast of Portugal, where a ten-thousand-mile undersea cable just two thumbs wide connects Europe and Africa, to the wilds of the Pacific Northwest, where Google, Microsoft, and Facebook have built monumental data centers—Blum chronicles the dramatic story of the Internet's development, explains how it all works, and takes the first-ever in-depth look inside its hidden monuments.This is a book about real places on the map: their sounds and smells, their storied pasts, their physical details, and the people who live there. For all the talk of the "placelessness" of our digital age, the Internet is as fixed in real, physical spaces as the railroad or telephone. You can map it and touch it, and you can visit it. Is the Internet in fact "a series of tubes" as Ted Stevens, the late senator from Alaska, once famously described it? How can we know the Internet's possibilities if we don't know its parts?Like Tracy Kidder's classic The Soul of a New Machine or Tom Vanderbilt's recent bestseller Traffic, Tubes combines on-the-ground reporting and lucid explanation into an engaging, mind-bending narrative to help us understand the physical world that underlies our digital lives.

    And through CODE, we see how this ingenuity and our very human compulsion to communicate have driven the technological innovations of the past two centuries. Using everyday objects and familiar language systems such as Braille and Morse code, author Charles Petzold weaves an illuminating narrative for anyone who’s ever wondered about the secret inner life of computers and other smart machines. It’s a cleverly illustrated and eminently comprehensible story—and along the way, you’ll discover you’ve gained a real context for understanding today’s world of PCs, digital media, and the Internet. No matter what your level of technical savvy, CODE will charm you—and perhaps even awaken the technophile within.

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    Joining the professional web designers and programmers are new audiences who need to know a little bit of code at work (update a content management system or e-commerce store) and those who want to make their personal blogs more attractive. Many books teaching HTML and CSS are dry and only written for those who want to become programmers, which is why this book takes an entirely new approach. • Introduces HTML and CSS in a way that makes them accessible to everyone—hobbyists, students, and professionals—and it’s full-color throughout • Utilizes information graphics and lifestyle photography to explain the topics in a simple way that is engaging • Boasts a unique structure that allows you to progress through the chapters from beginning to end or just dip into topics of particular interest at your leisureThis educational book is one that you will enjoy picking up, reading, then referring back to. It will make you wish other technical topics were presented in such a simple, attractive and engaging way!

    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.

    But if code isn't clean, it can bring a development organization to its knees. Every year, countless hours and significant resources are lost because of poorly written code. But it doesn't have to be that way. Noted software expert Robert C. Martin presents a revolutionary paradigm with Clean Code: A Handbook of Agile Software Craftsmanship . Martin has teamed up with his colleagues from Object Mentor to distill their best agile practice of cleaning code on the fly into a book that will instill within you the values of a software craftsman and make you a better programmer but only if you work at it. What kind of work will you be doing? You'll be reading code - lots of code. And you will be challenged to think about what's right about that code, and what's wrong with it. More importantly, you will be challenged to reassess your professional values and your commitment to your craft. Clean Code is divided into three parts. The first describes the principles, patterns, and practices of writing clean code. The second part consists of several case studies of increasing complexity. Each case study is an exercise in cleaning up code - of transforming a code base that has some problems into one that is sound and efficient. The third part is the payoff: a single chapter containing a list of heuristics and "smells" gathered while creating the case studies. The result is a knowledge base that describes the way we think when we write, read, and clean code. Readers will come away from this book understanding ‣ How to tell the difference between good and bad code‣ How to write good code and how to transform bad code into good code‣ How to create good names, good functions, good objects, and good classes‣ How to format code for maximum readability ‣ How to implement complete error handling without obscuring code logic ‣ How to unit test and practice test-driven development This book is a must for any developer, software engineer, project manager, team lead, or systems analyst with an interest in producing better code.

    Lean Analytics steers you in the right direction.This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Packed with more than thirty case studies and insights from over a hundred business experts, Lean Analytics provides you with hard-won, real-world information no entrepreneur can afford to go without.Understand Lean Startup, analytics fundamentals, and the data-driven mindsetLook at six sample business models and how they map to new ventures of all sizesFind the One Metric That Matters to youLearn how to draw a line in the sand, so you’ll know it’s time to move forwardApply Lean Analytics principles to large enterprises and established products

    Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.

    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.

    I loved the tutorial-style game-like program development. This book rekindled my earliest joys of programming. Plus, JavaScript!" —Brendan Eich, creator of JavaScriptJavaScript is the language of the Web, and it's at the heart of every modern website from the lowliest personal blog to the mighty Google Apps. Though it's simple for beginners to pick up and play with, JavaScript is not a toy—it's a flexible and complex language, capable of much more than the showy tricks most programmers use it for.Eloquent JavaScript goes beyond the cut-and-paste scripts of the recipe books and teaches you to write code that's elegant and effective. You'll start with the basics of programming, and learn to use variables, control structures, functions, and data structures. Then you'll dive into the real JavaScript artistry: higher-order functions, closures, and object-oriented programming.Along the way you'll learn to:Master basic programming techniques and best practices Harness the power of functional and object-oriented programming Use regular expressions to quickly parse and manipulate strings Gracefully deal with errors and browser incompatibilities Handle browser events and alter the DOM structure Most importantly, Eloquent JavaScript will teach you to express yourself in code with precision and beauty. After all, great programming is an art, not a science—so why settle for a killer app when you can create a masterpiece?

     If you have ever found yourself lost halfway through other introductory materials on this topic, this is the book for you. If you don't understand set terminology such as vectors, hyperplanes, and centroids, then this is also the book for you. This starter course isn't a picture story book but does include many visual examples that break algorithms down into a digestible and practical format. As a starter course, this book connects the dots and offers the crash course I wish I had when I first started. The kind of guide I wish had before I started taking on introductory courses that presume you’re two days away from an advanced mathematics exam. That’s why this introductory course doesn’t go further on the subject than other introductory books, but rather, goes a step back. A half-step back in order to help everyone make his or her first strides in machine learning and is an ideal study companion for the visual learner. In this step-by-step guide you will learn: - How to download free datasets - What tools and software packages you need - Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data - Preparing data for analysis, including k-fold Validation - Regression analysis to create trend lines - Clustering, including k-means and k-nearest Neighbors - Naive Bayes Classifier to predict new classes - Anomaly detection and SVM algorithms to combat anomalies and outliers - The basics of Neural Networks - Bias/Variance to improve your machine learning model - Decision Trees to decode classification Please feel welcome to join this starter course by buying a copy, or sending a free sample to your preferred device.

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    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.

    It is a conversational book that feels easy and informative. This short and lucid book covers everything important, with concrete examples, and invites the reader to join this field. The chapters in the book are organized for a typical one-semester course. The book contains case-lets from real-world stories at the beginning of every chapter. There is a running case study across the chapters as exercises. This book is designed to provide a student with the intuition behind this evolving area, along with a solid toolset of the major data mining techniques and platforms. Students across a variety of academic disciplines, including business, computer science, statistics, engineering, and others are attracted to the idea of discovering new insights and ideas from data. This book can also be gainfully used by executives, managers, analysts, professors, doctors, accountants, and other professionals to learn how to make sense of the data coming their way. This is a lucid flowing book that one can finish in one sitting, or can return to it again and again for insights and techniques. Table of Contents Chapter 1: Wholeness of Business Intelligence and Data Mining Chapter 2: Business Intelligence Concepts & Applications Chapter 3: Data Warehousing Chapter 4: Data Mining Chapter 5: Decision Trees Chapter 6: Regression Models Chapter 7: Artificial Neural Networks Chapter 8: Cluster Analysis Chapter 9: Association Rule Mining Chapter 10: Text Mining Chapter 11: Web Mining Chapter 12: Big Data Chapter 13: Data Modeling Primer Appendix: Data Mining Tutorial using Weka

    This book fills that gap. It explains the process of writing good code, and teaches you to achieve beautifully programmed ends by way of extremely practical means. What It's About99 Bottles of OOP is a practical guide to writing cost-effective, maintainable, and pleasing object-oriented code. It explores: Recognizing when code is "good enough"Getting the best value from Test-Driven Development (TDD)Doing proper refactoring, not random "rehacktoring"Locating concepts buried in codeFinding names that convey deeper meaning Safely altering code by following the "Flocking Rules" Simplifying new additions with the Open/Closed PrincipleAvoiding conditionals by obeying the Liskov Substitution PrincipleMaking targeted improvements by reducing Code SmellsWhat Makes It Unique?We are practical people. We love beautiful code but we're committed to getting things done. 99 Bottles of OOP enables both of these desires. It teaches a practical technique for getting things done that leads, naturally and inevitably, to beautiful code.This book contains an extended refactoring, and it details the rationale behind every change. It is a hands-on workbook rather than a list of theoretical ideas. It explains how to use the principles of object-oriented design to guide, not just the final arrangement of code, but each decision about what line of code to write next.It teaches the theory of what good OO looks like, but even better, it supplies step-by-step guidance about how to achieve it.Who Should Read It?The lessons work for programmers with a broad range of experience, from rank novice to grizzled veteran. The code examples are written in Ruby, but this book is not about Ruby--it's about object-oriented programming and design. Regardless of your background, applying these techniques will make your code easier to understand, simpler to change, and more satisfying to contemplate.