During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    Get on the road to your best selfA meal planner companion to the New York Times bestselling The Skinnytaste Cookbook, this 52-week journal will help you take an organized, proactive approach toward the lifestyle you want.   • PLAN MEALS: look ahead and decide to eat healthy all week; choose snacks to pack for each day    • TRACK CALORIES OR POINTS: count what you take in so that you know what you’re really eating; compare tallies to your goals in ordeer to make progress    • LOG EXERCISE: pick an activity to do each day; note the calories you burned  With 20 Skinnytaste recipes, plus inspirational quotes and tips about superfoods, The Skinnytaste Meal Planner can guide you to becoming your best self.

    Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full-color book, data visualization guru and author Nathan Yau uses step-by-step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships.Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers Details tools that can be used to visualize data-native graphics for the Web, such as ActionScript, Flash libraries, PHP, and JavaScript and tools to design graphics for print, such as R and Illustrator Contains numerous examples and descriptions of patterns and outliers and explains how to show them Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing.

    After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layerperceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

    What many users don't realize is that the deceptively simple components that make Google so easy to use are the same features that generously unlock security flaws for the malicious hacker. Vulnerabilities in website security can be discovered through Google hacking, techniques applied to the search engine by computer criminals, identity thieves, and even terrorists to uncover secure information. This book beats Google hackers to the punch, equipping web administrators with penetration testing applications to ensure their site is invulnerable to a hacker's search. Penetration Testing with Google Hacks explores the explosive growth of a technique known as "Google Hacking." When the modern security landscape includes such heady topics as "blind SQL injection" and "integer overflows," it's refreshing to see such a deceptively simple tool bent to achieve such amazing results; this is hacking in the purest sense of the word. Readers will learn how to torque Google to detect SQL injection points and login portals, execute port scans and CGI scans, fingerprint web servers, locate incredible information caches such as firewall and IDS logs, password databases, SQL dumps and much more - all without sending a single packet to the target Borrowing the techniques pioneered by malicious "Google hackers," this talk aims to show security practitioners how to properly protect clients from this often overlooked and dangerous form of informationleakage. *First book about Google targeting IT professionals and security leaks through web browsing. *Author Johnny Long, the authority on Google hacking, will be speaking about "Google Hacking" at the Black Hat 2004 Briefing. His presentation on penetrating security flaws with Google is expected to create a lot of buzz and exposure for the topic. *Johnny Long's Web site hosts the largest repository of Google security exposures and is the most popular destination for security professionals who want to learn about the dark side of Google.

    "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, the author uses the flexible Python programming language to show how to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

    Max explains to you why programmers suck, and how to suck less as a programmer. There's just too much complex stuff in the world. Complex stuff can't be used, and it breaks too easily. Complexity is stupid. Simplicity is smart.Understanding Software covers many areas of programming, from how to write simple code to profound insights into programming, and then how to suck less at what you do! You'll discover the problems with software complexity, the root of its causes, and how to use simplicity to create great software. You'll examine debugging like you've never done before, and how to get a handle on being happy while working in teams.Max brings a selection of carefully crafted essays, thoughts, and advice about working and succeeding in the software industry, from his legendary blog Code Simplicity. Max has crafted forty-three essays which have the power to help you avoid complexity and embrace simplicity, so you can be a happier and more successful developer.Max's technical knowledge, insight, and kindness, has earned him code guru status, and his ideas will inspire you and help refresh your approach to the challenges of being a developer. What you will learn See how to bring simplicity and success to your programming world Clues to complexity - and how to build excellent software Simplicity and software design Principles for programmers The secrets of rockstar programmers Max's views and interpretation of the Software industry Why Programmers suck and how to suck less as a programmer Software design in two sentences What is a bug? Go deep into debugging About the Author Max Kanat-Alexander is the Technical Lead for Code Health at Google, where he does various work that helps other software engineers be more productive, including writing developer tools, creating educational programs, guiding refactoring efforts, and more.His roles at Google have included Tech Lead for YouTube on the Xbox, work on the Java JDK, JVM, and other aspects of Java for Google, and Technical Lead for Engineering Practices for YouTube, where he's supported developers across all of YouTube in best practices and engineering productivity. Max is a former Chief Architect of the Bugzilla Project, where he was one of the two main developers of the well-known Bugzilla Bug-Tracking System, used by thousands of organizations worldwide. Max also writes the legendary programming industry blog, Code Simplicity, where he challenges Complexity and embraces Simplicity for the programming industry.Max has been involved for several years at Google with enabling developers to work more effectively and helping shape engineering practice, and in this highly readable collection of essays you can share the best of his experience. Table of Contents Part One: Principles for Programmers Part Two: Software Complexity and its Causes Part Three: Simplicity and Software Design Part Four: Debugging Part Five:

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    Python is easy to learn, and it's perfect for exploring topics like statistics, geometry, probability, and calculus. You’ll learn to write programs to find derivatives, solve equations graphically, manipulate algebraic expressions, even examine projectile motion.Rather than crank through tedious calculations by hand, you'll learn how to use Python functions and modules to handle the number crunching while you focus on the principles behind the math. Exercises throughout teach fundamental programming concepts, like using functions, handling user input, and reading and manipulating data. As you learn to think computationally, you'll discover new ways to explore and think about math, and gain valuable programming skills that you can use to continue your study of math and computer science.If you’re interested in math but have yet to dip into programming, you’ll find that Python makes it easy to go deeper into the subject—let Python handle the tedious work while you spend more time on the math.

    The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it.In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go.Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.