How to Heal Your Metabolism will question everything you thought you knew about health and nutrition. If you are ready to understand nutrition and health in a completely different light, then you need to read this book.

    Its popularity among open-source developers makes Git a necessary tool for professional programmers, but it can also do wonders for your personal coding workflow. You’ll be able to experiment with new ideas, radically refactor existing code, and efficiently share changes with other developers—all without the slightest worry towards breaking your project.This comprehensive guide will walk you through the entire Git library, writing code and executing commands every step of the way. You'll create commits, revert snapshots, navigate branches, communicate with remote repositories, and experience core Git concepts first-hand.Designed for newcomers to distributed development, Ry's Git Tutorial presents this complex subject in simple terms that anyone can understand. Beginner and veteran programmers alike will find this book to be a fun, fast, and friendly introduction to Git-based revision control.

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    With this guide, pioneering Go programmer Mark Summerfield shows how to write code that takes full advantage of Go's breakthrough features and idioms. Both a tutorial and a language reference, "Programming in Go" brings together all the knowledge you need to evaluate Go, think in Go, and write high-performance software with Go. Summerfield presents multiple idiom comparisons showing exactly how Go improves upon older languages, calling special attention to Go's key innovations. Along the way, he explains everything from the absolute basics through Go's lock-free channel-based concurrency and its flexible and unusual duck-typing type-safe approach to object-orientation. Throughout, Summerfield's approach is thoroughly practical. Each chapter offers multiple live code examples designed to encourage experimentation and help you quickly develop mastery. Wherever possible, complete programs and packages are presented to provide realistic use cases, as well as exercises. Coverage includes:-- Quickly getting and installing Go, and building and running Go programs -- Exploring Go's syntax, features, and extensive standard library -- Programming Boolean values, expressions, and numeric types -- Creating, comparing, indexing, slicing, and formatting strings -- Understanding Go's highly efficient built-in collection types: slices and maps -- Using Go as a procedural programming language -- Discovering Go's unusual and flexible approach to object orientation -- Mastering Go's unique, simple, and natural approach to fine-grained concurrency -- Reading and writing binary, text, JSON, and XML files -- Importing and using standard library packages, custom packages, and third-party packages -- Creating, documenting, unit testing, and benchmarking custom packages

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

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    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.

    A dirty little secret assembly language programmers rarely admit to, however, is that what you really need to learn is machine organization, not assembly language programming. Write Great Code Vol I, the first in a series from assembly language expert Randall Hyde, dives right into machine organization without the extra overhead of learning assembly language programming at the same time. And since Write Great Code Vol I concentrates on the machine organization, not assembly language, the reader will learn in greater depth those subjects that are language-independent and of concern to a high level language programmer. Write Great Code Vol I will help programmers make wiser choices with respect to programming statements and data types when writing software, no matter which language they use.

    Jason Briggs, author of the popular online tutorial "Snake Wrangling for Kids," begins with the basics of how to install Python and write simple commands. In bite-sized chapters, he instructs readers on the essentials of Python, including how to use Python's extensive standard library, the difference between strings and lists, and using for-loops and while-loops. By the end of the book, readers have built a game and created drawings with Python's graphics library, Turtle. Each chapter closes with fun and relevant exercises that challenge the reader to put their newly acquired knowledge to the test.

    

    That makes it by far the world's biggest selling books, outside the Bible, for 1999! This new edition, now available in America, is completely revised and tells the story of the learning revolution that is need to match the revolution in communications and technology. It presents the world's best research on how every one of us, at any age, can make that revolution happen now. And it tells it in a language that is as easy to read as The Reader's Digest. Among the topics covered: * How to learn anything much faster, better and more easily. * How to read four books a day and remember what you read. * How to learn the basics of a new language in 8 weeks. * How to bridge a 5 year reading gap in 10 weeks. * How to read and write before starting school. * How to make the most of your amazing brain. * How to Mind Map your way to better grades. * How to create successful, profitable ideas. * How to use the world's best learning technology.

    It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence. This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving. We cover abstract data types and data structures, writing algorithms, and solving problems. We look at a number of data structures and solve classic problems that arise. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

    This is a book about LEARNING!Technology is not a magic solution for education. It is an opportunity! An opportunity to shake things up, to connect and grow, and to create dynamic learning experiences for our students! In this three-part book, you will explore WHY it's time to Shake Up Learning, WHAT changes we can make in our classrooms to support dynamic learning experiences, and HOW to plan meaningful lessons for your classroom.Rapidly evolving technology and the demands of the digital age are transforming not only the way we live but also the way we learn. The tools students are using are newer, sleeker, and faster than ever before. In some cases, the medium is even changing the message. One thing is certain: Educators cannot continue the status quo if they expect to equip young people for the world to come. Here's the good news: With digital tools that are available 24/7, learning doesn't have to stop when the bell rings. Learning can take on a life of its own! Even better, technology can help you connect with students and empower them to grow and develop a lifelong love for learning--and it doesn't have to be scary or complicated. Shake Up Learning is a powerful guide and planning tool to help educators at all grade levels make the most of technology. Educator and blogger Kasey Bell guides you through the process of using technology and proven techniques to make learning dynamic. You'll discover . . . Practical strategies to help move from static teaching to dynamic learningStraightforward and easy-to-use templates for crafting engaging learning opportunitiesTips and tricks for fearless implementation of powerful lesson plansAdvice for moving from one-and-done activities to learning that evolves and inspires throughout the school year--and beyond!This is MORE than just a book! This is a full LEARNING EXPERIENCE!This book is jammed packed with ideas, lessons, and resources, but you can bring it all to life with the companion website, ShakeUpLearningBook.com, and the companion online course, The Dynamic Learning Workshop!Be dynamic! Shake up learning in your classroom this year.

    Like earlier editions of the book, physical concepts are highlighted while also giving necessary attention to math-ematical techniques. Both theory and modeling are developed from simple beginnings so that they can be readily extended to new and complex situations. Beginning in Ch. 3, students are introduced to new concepts critical to analyzing power systems, including coverage of both balanced and unbalanced operating conditions. The authors incorporate new tools and material to aid students with design issues and reflect recent trends in the field. Each book now contains a CD with Power World software. This package is commonly used in industry and will enable students to analyze and simulate power systems. The authors use the software to extend, rather than replace, the fully worked examples provided in previous editions. In the new edition, each Power World Simulator example includes a fully worked hand solution of the problem along with a Power World Simulator case (except when the problem size makes it impractical). The new edition also contains updated case studies on recent trends in the Power Systems field, including coverage of deregulation, increased power demand, economics, and alternative sources of energy. These case studies are derived from real life situations.

    When it came time to select high schools, he thought it worth doing some investigation to assess the level of advantage his kids would enjoy if he spent the required $1.3 million to send them all to private schools.Shockingly, the answer was: none whatsoever.Intrigued, David continued his research, only to discover he was wrong on most counts - as are most parents - when it comes to working out what factors deliver a great education. He discovered that class size doesn't matter, your kids aren't any better off in co-ed than single-sex schools (and vice versa), composite classes are fine, fancy buildings are a waste of money, the old-tie network won't cut it in the new industries and NAPLAN is misread by everyone so is largely meaningless as a measure of quality.Taking on an ingrained and historical system of vested interests - the unions, the government, our own sense of worth, privilege and entitlement - this book is controversial and absolutely necessary. It is well researched, authoritative and accessible. It is a must-read for parents, as well as teachers and policy-makers.