They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own machine learning algorithms in Python, this book is for you.

    This book is an introduction to the concepts of computer programming within the context of the visual arts. It offers a comprehensive reference and text for Processing (www.processing.org), an open-source programming language that can be used by students, artists, designers, architects, researchers, and anyone who wants to program images, animation, and interactivity. The ideas in Processing have been tested in classrooms, workshops, and arts institutions, including UCLA, Carnegie Mellon, New York University, and Harvard University. Tutorial units make up the bulk of the book and introduce the syntax and concepts of software (including variables, functions, and object-oriented programming), cover such topics as photography and drawing in relation to software, and feature many short, prototypical example programs with related images and explanations. More advanced professional projects from such domains as animation, performance, and typography are discussed in interviews with their creators. "Extensions" present concise introductions to further areas of investigation, including computer vision, sound, and electronics. Appendixes, references to additional material, and a glossary contain additional technical details. Processing can be used by reading each unit in order, or by following each category from the beginning of the book to the end. The Processing software and all of the code presented can be downloaded and run for future exploration.Includes essays by Alexander R. Galloway, Golan Levin, R. Luke DuBois, Simon Greenwold, Francis Li, and Hernando Barragan and interviews with Jared Tarbell, Martin Wattenberg, James Paterson, Erik van Blockland, Ed Burton, Josh On, Jurg Lehni, Auriea Harvey and Michael Samyn, Mathew Cullen and Grady Hall, Bob Sabiston, Jennifer Steinkamp, Ruth Jarman and Joseph Gerhardt, Sue Costabile, Chris Csikszentmihalyi, Golan Levin and Zachary Lieberman, and Mark Hansen.Casey Reas is Associate Professor in the Design Media Arts Department at the University of California, Los Angeles. Ben Fry is Nierenburg Chair of Design in the School of Design at Carnegie Mellon University, 2006-2007."

    

    Short chapters are paired with free interactive online exercises to teach the fundamentals of HTML and CSS. Written for beginners, useful for experienced developers who want to sharpen their skills. Prepares the reader to code a website of medium complexity. The learner spends two to three times as long practicing as he does reading. Based on cognitive research showing that retention increases 400 percent when learners are challenged to retrieve the information they just read. Explanations are in plain, nontechnical English that people of all backgrounds can readily understand. With ample coding examples and illustrations.

     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.

      Writing a college admissions essay is no easy task—but with college essay coach and New York Times contributor Alan Gelb’s accessible and encouraging step-by-step instructions, you’ll be able to write an honest, one-of-a-kind essay that really shines. Gelb’s ten-step approach has garnered great results for the students who have tried it, many of whom were accepted into their dream schools (Harvard, Brown, Yale, and more). This to-the-point handbook shows you how to identify an engaging essay topic, and then teaches you how to use creative writing techniques to craft a narrative that expresses your unique personality, strengths, and goals. Whether you’re an A-student looking for an extra boost or a less-confident writer who needs more intensive help, Gelb’s reassuring and concise guidance will help you every step of the way, from your initial draft to final revision. In the end, you will have a well-polished, powerful, and profound personal statement that you can feel proud of—a college essay that doesn’t feel “pre-fab,” but is a real reflection of your own individuality.

    With the many upgraded features, C# is more expressive than ever. However, an in depth understanding is required to get the most out of the language.C# in Depth, Second Edition is a thoroughly revised, up-to-date book that covers the new features of C# 4 as well as Code Contracts. In it, you'll see the subtleties of C# programming in action, learning how to work with high-value features that you'll be glad to have in your toolkit. The book helps readers avoid hidden pitfalls of C# programming by understanding "behind the scenes" issues.Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

    Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    It contains sample programs to illustrate practical solutions to the type of real-world problems professional developers encounter.

    The main difference is that our lessons put student curiosity and engagement first. -Harvey Smokey Daniels and Nancy SteinekeIn this highly anticipated follow-up to Texts and Lessons for Content-Area Reading, Harvey Smokey Daniels and Nancy Steineke share their powerful strategies for engaging students in challenging, meaningful reading of fiction and poetry using some of their favorite short, fresh texts-or, as they put it, full-strength adult literature that gives us English majors a run for our interpretive money- but is still intriguing enough to keep teen readers digging and thinking. Use the 37 innovative, step-by-step, common-core-correlated lessons with the reproducible texts provided, with selections from your literature textbook, or with your own best-loved texts to teach close reading skills and deep comprehension strategies. Give students opportunities to read and synthesize across texts with the 8 thematic text set lessons provided, or use the model unit outlines for using the lessons with The Giver, To Kill a Mockingbird, and The Great Gatsby as springboards for planning your own novel studies. Better Together! Used together, Texts and Lessons for Teaching Literature and Texts and Lessons for Content-Area Reading give you all the lesson ideas you need for all text types. Save 15% when you buy them together in a Texts and Lessons Bundle.

    Introducing students to the many facets of neural networks, this text provides many case studies to illustrate their real-life, practical applications.

    Why is Christ called the “Son of God”? Discover an answer so simple you’ll wonder why you never saw it before, and so beautiful it’ll take your breath away.

    "Basics of Web Design: HTML5 and CSS3, 2e "covers the basic concepts that web designers need to develop their skills: Introductory Internet and Web concepts Creating web pages with HTML5 Configuring text, color, and page layout with Cascading Style Sheets Configuring images and multimedia on web pages Web design best practices Accessibility, usability, and search engine optimization considerations Obtaining a domain name and web host Publishing to the Web

    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.