Unlike other introductory books, it focuses on the program design process. This approach fosters a variety of skills--critical reading, analytical thinking, creative synthesis, and attention to detail--that are important for everyone, not just future computer programmers. The book exposes readers to two fundamentally new ideas. First, it presents program design guidelines that show the reader how to analyze a problem statement; how to formulate concise goals; how to make up examples; how to develop an outline of the solution, based on the analysis; how to finish the program; and how to test. Each step produces a well-defined intermediate product. Second, the book comes with a novel programming environment, the first one explicitly designed for beginners. The environment grows with the readers as they master the material in the book until it supports a full-fledged language for the whole spectrum of programming tasks.All the book's support materials are available for free on the Web. The Web site includes the environment, teacher guides, exercises for all levels, solutions, and additional projects.A second edition is now available.

    It's certainly no walk in the park. And, depending on where you've been for your information when it comes to SEO, it might be outdated, or just flat-out wrong. Why is that? Search has been evolving at an uncanny rate in recent years. And, if you're not in the know, then you could end up spinning your wheels and wasting valuable and precious time and resources on techniques that no longer work. The main reason for the recent changes: to increase relevancy. Google's sole mission is to provide the most relevant search results at the top of its searches, in the quickest manner possible. But, in recent years, due to some mischievous behavior at the hand of a small group of people, relevancy began to wane. SEO 2016 :: Understanding Google's Algorithm Adjustments The field of SEO has been changing, all led by Google's onslaught of algorithm adjustments that have decimated and razed some sites while uplifting and building others. Since 2011, Google has made it its mission to hunt out and demote spammy sites that sacrifice user-experience, focus on thin content, or simply spend their time trying to trick and deceive their way to the top of its search results. At the same time, Google has increased its reliance on four major components of trust, that work at the heart of its search algorithm: Trust in Age Trust in Authority Trust in Content Relevancy In this book, you'll learn just how each of these affects Google's search results, and just how you can best optimize your site and content to ensure that you're playing by Google's many rules. And, although there have been many algorithm adjustments over the years, four major ones have shaped and forever changed the search engine landscape: Google Panda Google Penguin Google Hummingbird Google Mobilegeddon We'll discuss the nature of these changes and just how each of these algorithm adjustments have shaped the current landscape in search engine optimization. So what does it take to rank your site today? In order to compete at any level in SEO, you have to earn trust - Google's trust that is. But, what does that take? How can we build trust quickly without jumping through all the hoops? SEO is by no means a small feat. It takes hard work applied consistently overtime. There are no overnight success stories when it comes to SEO. But there are certainly ways to navigate the stormy online waters of Google's highly competitive search. Download SEO 2016 :: Learn Search Engine Optimization Lift the veil on Google's complex search algorithm, and understand just what it takes to rank on Google searches today, not yesterday.

    

    Kubernetes has emerged as the hottest and most important container orchestration platform in the world. This book gets you up to speed fast, and it's constantly kept up-to-date!

    It presents the principles, paradigms, designs and implementations of modern programming languages, and contains increased coverage of the object-oriented programming paradigm. The book also covers semantics and Java.

    Perl Cookbook is a comprehensive collection of problems, solutions, and practical examples for anyone programming in Perl. The book contains hundreds of rigorously reviewed Perl "recipes" and thousands of examples ranging from brief one-liners to complete applications.The second edition of Perl Cookbook has been fully updated for Perl 5.8, with extensive changes for Unicode support, I/O layers, mod_perl, and new technologies that have emerged since the previous edition of the book. Recipes have been updated to include the latest modules. New recipes have been added to every chapter of the book, and some chapters have almost doubled in size.Covered topic areas include: • Manipulating strings, numbers, dates, arrays, and hashes • Pattern matching and text substitutions • References, data structures, objects, and classes • Signals and exceptions • Screen addressing, menus, and graphical applications • Managing other processes • Writing secure scripts • Client-server programming • Internet applications programming with mail, news, ftp, and telnet • CGI and mod_perl programming • Web programmingSince its first release in 1998, Perl Cookbook has earned its place in the libraries of serious Perl users of all levels of expertise by providing practical answers, code examples, and mini-tutorials addressing the challenges that programmers face. Now the second edition of this bestselling book is ready to earn its place among the ranks of favorite Perl books as well.Whether you're a novice or veteran Perl programmer, you'll find Perl Cookbook, 2nd Edition to be one of the most useful books on Perl available. Its comfortable discussion style and accurate attention to detail cover just about any topic you'd want to know about. You can get by without having this book in your library, but once you've tried a few of the recipes, you won't want to.

    The book focuses on using SQL to find the story your data tells, with the popular open-source database PostgreSQL and the pgAdmin interface as its primary tools.You'll first cover the fundamentals of databases and the SQL language, then build skills by analyzing data from the U.S. Census and other federal and state government agencies. With exercises and real-world examples in each chapter, this book will teach even those who have never programmed before all the tools necessary to build powerful databases and access information quickly and efficiently.You'll learn how to: •Create databases and related tables using your own data •Define the right data types for your information •Aggregate, sort, and filter data to find patterns •Use basic math and advanced statistical functions •Identify errors in data and clean them up •Import and export data using delimited text files •Write queries for geographic information systems (GIS) •Create advanced queries and automate tasks Learning SQL doesn't have to be dry and complicated. Practical SQL delivers clear examples with an easy-to-follow approach to teach you the tools you need to build and manage your own databases. This book uses PostgreSQL, but the SQL syntax is applicable to many database applications, including Microsoft SQL Server and MySQL.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    A whole generation was introduced to object technology through the first edition of this book. This long-awaited new edition retains the qualities of clarity, practicality and scholarship that made the first an instant bestseller, but has been thoroughly revised and expanded.Among the new topics covered in depth are: concurrency, distribution, client/server and the Internet, object-oriented databases, design by contract, fundamental design patterns, finding classes, the use and misuse of inheritance, abstract data types, and typing issues. The book also includes completely updated discussions of reusability, modularity, software quality, object-oriented languages, memory management, and many other essential topics.

    It's as if we think of code as a magic spell, an incantation to reveal what we need to know and even what we want. Humans have always believed that certain invocations—the marriage vow, the shaman's curse—do not merely describe the world but make it. Computation casts a cultural shadow that is shaped by this long tradition of magical thinking. In this book, Ed Finn considers how the algorithm—in practical terms, “a method for solving a problem”—has its roots not only in mathematical logic but also in cybernetics, philosophy, and magical thinking.Finn argues that the algorithm deploys concepts from the idealized space of computation in a messy reality, with unpredictable and sometimes fascinating results. Drawing on sources that range from Neal Stephenson's Snow Crash to Diderot's Encyclopédie, from Adam Smith to the Star Trek computer, Finn explores the gap between theoretical ideas and pragmatic instructions. He examines the development of intelligent assistants like Siri, the rise of algorithmic aesthetics at Netflix, Ian Bogost's satiric Facebook game Cow Clicker, and the revolutionary economics of Bitcoin. He describes Google's goal of anticipating our questions, Uber's cartoon maps and black box accounting, and what Facebook tells us about programmable value, among other things.If we want to understand the gap between abstraction and messy reality, Finn argues, we need to build a model of “algorithmic reading” and scholarship that attends to process, spearheading a new experimental humanities.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    10 illustrations.

    Simply stated, it asks whether every problem whose solution can be quickly checked by computer can also be quickly solved by computer. The Golden Ticket provides a nontechnical introduction to P-NP, its rich history, and its algorithmic implications for everything we do with computers and beyond. Lance Fortnow traces the history and development of P-NP, giving examples from a variety of disciplines, including economics, physics, and biology. He explores problems that capture the full difficulty of the P-NP dilemma, from discovering the shortest route through all the rides at Disney World to finding large groups of friends on Facebook. The Golden Ticket explores what we truly can and cannot achieve computationally, describing the benefits and unexpected challenges of this compelling problem.

     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.

     Data Structures and Algorithms in Python is the first mainstream object-oriented book available for the Python data structures course. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++.