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.

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

    This book will teach you how to use bash's advanced command-line features, such as command history, command-line editing, and command completion.This book also introduces shell programming,a skill no UNIX or Linus user should be without. The book demonstrates what you can do with bash's programming features. You'll learn about flow control, signal handling, and command-line processing and I/O. There is also a chapter on debugging your bash programs.Finally, Learning the bash Shell, Third Edition, shows you how to acquire, install, configure, and customize bash, and gives advice to system administrators managing bash for their user communities.This Third Edition covers all of the features of bash Version 3.0, while still applying to Versions 1.x and 2.x. It includes a debugger for the bash shell, both as an extended example and as a useful piece of working code. Since shell scripts are a significant part of many software projects, the book also discusses how to write maintainable shell scripts. And, of course, it discusses the many features that have been introduced to bash over the years: one-dimensional arrays, parameter expansion, pattern-matching operations, new commands, and security improvements.Unfailingly practical and packed with examples and questions for future study, Learning the bash Shell Third Edition is a valuable asset for Linux and other UNIX users.--back cover

    No age, time, place, or body constraints can prevent you from becoming the perfect lover you desire to be for the rest of your life. These pages unlock all of the essential elements for attaining this goal.- Learn and develop sexual magnetism and secrets that few lovers have ever known.- Become an irresistible, fascinating, unforgettable lover.- Overcome sexual blocks and inhibitions.- Achieve multiple orgasms.- Discover how to stimulate a womanAs G-spot.- Practice Kama Sutra techniques with amazing illustrations and personal tips from the author.- Make your relationship sacred; feel inner divinity while making love.- Find and attract your soul mate.A bestseller around the world, Sex and the Perfect Lover has become the definitive guide to improving your sexual experience. So effectively written by author Mabel Iam, this book will become your eternal companion and advisor; all of your needs and doubts about eroticism will be eased by IamAs tried-and-true advice. From your first kiss to sexual ecstasy, Mabel Iam sheds light on your being and guides you along the path to an intense pleasure that only love and sex can bring.

    This highly anticipated fourth edition of the most ...Download Link : readmeaway.com/download?i=0128042915            0128042915 Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF by Ian H. WittenRead Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF from Morgan Kaufmann,Ian H. WittenDownload Ian H. Witten's PDF E-book Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

    It has conquered developer mindshare at startups and enterprises alike with its focus of simplicity, convention and clean, maintainable code. The latest version, Rails 4, continues the tradition of enhanced performance, security and developer productivity, with improvements that enable professional developers to focus on what matters most: delivering business value quickly and consistently.The Rails™ 4 Way is the only comprehensive, authoritative guide to delivering production-quality code with Rails 4. Pioneering Rails expert Obie Fernandez and his team of leading Rails experts illuminate the entire set of Rails APIs, along with the idioms, design approaches, and libraries that make developing applications with Rails so powerful. Drawing on their unsurpassed experience and track record, they address the real challenges development teams face, showing how to use Rails to maximize your productivity.Using numerous detailed code examples, the author systematically cover Rails key capabilities and subsystems, making this book a reference that you depend on everyday. He presents advanced Rails programming techniques that have been proven effective in day-to-day usage on dozens of production Rails systems and offers important insights into behavior-driven development and production considerations such as scalability. Dive deep into the subtleties of the asset pipeline and other advanced Rails topics such as security and scalability. The Rails 4 Way is your best guide for making Rails do exactly what you want it to do.

    A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.

    It was the dawn of a new era in artificial intelligence: a machine capable of beating the reigning human champion at this most cerebral game. That moment was more than a century in the making, and in this breakthrough book, Kasparov reveals his astonishing side of the story for the first time. He describes how it felt to strategize against an implacable, untiring opponent with the whole world watching, and recounts the history of machine intelligence through the microcosm of chess, considered by generations of scientific pioneers to be a key to unlocking the secrets of human and machine cognition. Kasparov uses his unrivaled experience to look into the future of intelligent machines and sees it bright with possibility. As many critics decry artificial intelligence as a menace, particularly to human jobs, Kasparov shows how humanity can rise to new heights with the help of our most extraordinary creations, rather than fear them. Deep Thinking is a tightly argued case for technological progress, from the man who stood at its precipice with his own career at stake.

    Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business dataUpdates that incorporate input from readers, changes in the field, and more material on statistics and machine learningDozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projectsComplete classroom support for instructors at www.mkp.com/datamining2e companion site

    More comprehensive than ever before--and now 1,000 pages--this revised edition describes the insider dynamics at hundreds of U.S. and Canadian publishers, with hundreds of names and specialties for book acquisition editors. Nearly 200 of the most powerful literary agents reveal invaluable tips, as if they were having a private conversation with a special friend. With detailed information on what to do (and what not to do) to break the code, break down the walls, and get that first book, second book, or thirtieth book published, bought and read, Jeff Herman's Guide is the go-to source for writers everywhere.

    But building a machine learning system requires that you make practical decisions: Should you collect more training data? Should you use end-to-end deep learning? How do you deal with your training set not matching your test set? and many more. Historically, the only way to learn how to make these "strategy" decisions has been a multi-year apprenticeship in a graduate program or company. This is a book to help you quickly gain this skill, so that you can become better at building AI systems.

    Through example code, this guide demonstrates how CoffeeScript abstracts JavaScript, providing syntactical sugar and preventing many common errors. You’ll learn CoffeeScript’s syntax and idioms step by step, from basic variables and functions to complex comprehensions and classes.Written by Alex MacCaw, author of JavaScript Web Applications (O’Reilly), with contributions from CoffeeScript creator Jeremy Ashkenas, this book quickly teaches you best practices for using this language—not just on the client side, but for server-side applications as well. It’s time to take a ride with the little language that could.Discover how CoffeeScript’s syntax differs from JavaScriptLearn about features such as array comprehensions, destructuring assignments, and classesExplore CoffeeScript idioms and compare them to their JavaScript counterpartsCompile CoffeeScript files in static sites with the Cake build systemUse CommonJS modules to structure and deploy CoffeeScript client-side applicationsExamine JavaScript’s bad parts—including features CoffeeScript was able to fix

    Kai-Fu Lee—one of the world’s most respected experts on AI and China—reveals that China has suddenly caught up to the US at an astonishingly rapid and unexpected pace.In AI Superpowers, Kai-Fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to heat up, Lee urges the US and China to both accept and to embrace the great responsibilities that come with significant technological power.Most experts already say that AI will have a devastating impact on blue-collar jobs. But Lee predicts that Chinese and American AI will have a strong impact on white-collar jobs as well. Is universal basic income the solution? In Lee’s opinion, probably not.  But he provides a clear description of which jobs will be affected and how soon, which jobs can be enhanced with AI, and most importantly, how we can provide solutions to some of the most profound changes in human history that are coming soon.

    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.

    Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the intelligence of their human creators, defeating the world champion Go player, achieving superhuman performance on video games, driving cars, translating languages, and sometimes even helping law enforcement fight crime. Deep Learning is a revolution that is changing every industry across the globe.Grokking Deep Learning is the perfect place to begin your deep learning journey. Rather than just learn the “black box” API of some library or framework, you will actually understand how to build these algorithms completely from scratch. You will understand how Deep Learning is able to learn at levels greater than humans. You will be able to understand the “brain” behind state-of-the-art Artificial Intelligence. Furthermore, unlike other courses that assume advanced knowledge of Calculus and leverage complex mathematical notation, if you’re a Python hacker who passed high-school algebra, you’re ready to go. And at the end, you’ll even build an A.I. that will learn to defeat you in a classic Atari game.