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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlo...
by Aurélien Géron

Language

English

Pages

851

Publication Date

September 05, 2019

Product Description
Customer Reviews
<p>Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.</p><p>By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.</p><ul><li>Explore the machine learning landscape, particularly neural nets</li><li>Use Scikit-Learn to track an example machine-learning project end-to-end</li><li>Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods</li><li>Use the TensorFlow library to build and train neural nets</li><li>Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning</li><li>Learn techniques for training and scaling deep neural nets</li></ul>
Don't Make Me Think, Revisited: A Common Sense Approach to Web Us...
by Steve Krug

Language

English

Pages

210

Publication Date

December 23, 2013

Product Description
Customer Reviews
Since<i> Don’t Make Me Think </i>was first published in 2000, hundreds of thousands of Web designers and developers have relied on usability guru Steve Krug’s guide to help them understand the principles of intuitive navigation and information design. Witty, commonsensical, and eminently practical, it’s one of the best-loved and most recommended books on the subject.<br /> <br />Now Steve returns with fresh perspective to reexamine the principles that made<i> Don’t Make Me Think</i> a classic–with updated examples and a new chapter on mobile usability. And it’s still short, profusely illustrated…and best of all–fun to read.<br /> <br />If you’ve read it before, you’ll rediscover what made <i>Don’t Make Me Think</i> so essential to Web designers and developers around the world. If you’ve never read it, you’ll see why so many people have said it should be required reading for anyone working on Web sites.<br /> <br /> <br />“After reading it over a couple of hours and putting its ideas to work for the past five years, I can say it has done more to improve my abilities as a Web designer than any other book.”<br />–Jeffrey Zeldman, author of Designing with Web Standards <p> </p>
Data Science from Scratch: First Principles with Python
by Joel Grus

Language

English

Pages

408

Publication Date

April 12, 2019

Product Description
Customer Reviews
<p>To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.</p><p>If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.</p><ul><li>Get a crash course in Python</li><li>Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science</li><li>Collect, explore, clean, munge, and manipulate data</li><li>Dive into the fundamentals of machine learning</li><li>Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering</li><li>Explore recommender systems, natural language processing, network analysis, MapReduce, and databases</li></ul>
Database Internals: A Deep Dive into How Distributed Data Systems...
by Alex Petrov

Language

English

Pages

376

Publication Date

September 13, 2019

Product Description
Customer Reviews
<p>When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.</p><p>Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed.</p><p>This book examines:</p><ul><li><b>Storage engines:</b> Explore storage classification and taxonomy, and dive into B-Tree-based and immutable Log Structured storage engines, with differences and use-cases for each</li><li><b>Storage building blocks:</b> Learn how database files are organized to build efficient storage, using auxiliary data structures such as Page Cache, Buffer Pool and Write-Ahead Log</li><li><b>Distributed systems:</b> Learn step-by-step how nodes and processes connect and build complex communication patterns</li><li><b>Database clusters:</b> Which consistency models are commonly used by modern databases and how distributed storage systems achieve consistency</li></ul>
Swipe to Unlock: The Primer on Technology and Business Strategy
by , Neel Mehta

Language

English

Pages

347

Publication Date

September 19, 2017

Product Description
Customer Reviews
<b>Authored by 3 Product Managers at Google, Facebook, and Microsoft, </b><i>Swipe to Unlock</i> is a comprehensive guide on the must-know concepts of technology and business strategy. <b>It is a must-read for anyone pursuing product management, design, marketing, consulting or business strategy roles in the tech industry.</b><br /><br />***Swipe to Unlock was updated in 2019 to <b>include over 40 pages of new content</b> to cover the latest developments in the world of tech.***<b> We highly recommend buying our book directly from Amazon rather than a third-party seller as it's the only way to ensure you get the latest version.</b><br /><br />This <b>#1 Amazon Business Bestseller won a medal </b>from the <b>North American Book Awards</b> and has been featured in <b>The Wall Street Journal</b>, <b>Forbes</b>, and <b>Business Insider. </b><i>Swipe to Unlock</i> was touted as <b>"our generation's Rosetta Stone for enabling anyone to peer into the technology changing everyday life"</b> by Jeremy Schifeling.<br /><ul><li>How does Spotify determine what songs to recommend to you?</li><li>How did KaiOS become the third largest mobile operating system in just two years since launching?</li><li>Why does Amazon offer free shipping with Prime if it loses them money?</li><li>How did a single typo take down 20% of the internet?</li><li>Why did Microsoft acquire LinkedIn?</li></ul>By answering real-world questions like this, <b><i>Swipe to Unlock</i> gives you a peek under the hood of the technology you use every day, decodes tech's biggest buzzwords, and shows you how technology is changing the society we live in for better or for worse.</b><br /><br /><b>Topics Covered: </b>Software Development, Business Models & Strategies, Economics, Hacking & Security, Hardware & Robots, The Internet, Cloud Computing, Big Data, Technology Policy, Emerging Markets, Future Trends, and much more!<br /><br /><b>Featured Companies: </b>Google, Facebook, Microsoft, Amazon, Apple, Spotify, Uber, WeChat, Yelp, Tinder, Washington Post, Grab, Toyota, GoJek, Samsung, Salesforce, M-Pesa, Quora, KaiOS, Twitter, Tesla, ByteDance, Airbnb, Robinhood, Adobe, Alibaba, Netflix, Paytm, Target, and many more!<br /><br /><b>Unlock the answers you need to ace your tech interviews or become a better-educated digital citizen. Buy Swipe to Unlock today to understand why it's a Top Gifted Book of 2018!</b><br />
Data Mining for Business Analytics: Concepts, Techniques and Appl...
by , Nitin R. Patel

Language

English

Pages

608

Publication Date

October 28, 2019

Product Description
Customer Reviews
<p><b><i>Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python</i></b><b> presents an applied approach to data mining concepts and methods, using Python software for illustration</b></p> <p>Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities.</p> <p>This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:</p> <ul> <li>A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process</li> <li>A new section on ethical issues in data mining</li> <li>Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students</li> <li>More than a dozen case studies demonstrating applications for the data mining techniques described</li> <li>End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented</li> <li>A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions</li> </ul> <p><i>Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python</i> is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.</p> <p>“This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.”</p> <p>—Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book <i>An Introduction to Statistical Learning, with Applications in R </i></p>
Python Machine Learning: Machine Learning and Deep Learning with ...
by , Vahid Mirjalili

Language

English

Pages

770

Publication Date

December 12, 2019

Product Description
Customer Reviews
<p><b>Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.</b></p><h4>Key Features</h4><ul><li>Third edition of the bestselling, widely acclaimed Python machine learning book</li><li>Clear and intuitive explanations take you deep into the theory and practice of Python machine learning</li><li>Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices</li></ul><h4>Book Description</h4><p>Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.</p><p>Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.</p><p>Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.</p><p>This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.</p><h4>What you will learn</h4><ul><li>Master the frameworks, models, and techniques that enable machines to 'learn' from data</li><li>Use scikit-learn for machine learning and TensorFlow for deep learning</li><li>Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more</li><li>Build and train neural networks, GANs, and other models</li><li>Discover best practices for evaluating and tuning models</li><li>Predict continuous target outcomes using regression analysis</li><li>Dig deeper into textual and social media data using sentiment analysis</li></ul><h4>Who This Book Is For</h4><p>If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.</p> <h4>Table of Contents</h4><ol><li>Giving Computers the Ability to Learn from Data</li><li>Training Simple ML Algorithms for Classification</li><li>ML Classifiers Using scikit-learn</li><li>Building Good Training Datasets - Data Preprocessing</li><li>Compressing Data via Dimensionality Reduction</li><li>Best Practices for Model Evaluation and Hyperparameter Tuning</li><li>Combining Different Models for Ensemble Learning</li><li>Applying ML to Sentiment Analysis</li><li>Embedding a ML Model into a Web Application</li><li>Predicting Continuous Target Variables with Regression Analysis</li><li>Working with Unlabeled Data - Clustering Analysis</li><li>Implementing Multilayer Artificial Neural Networks</li><li>Parallelizing Neural Network Training with TensorFlow</li><li>TensorFlow Mechanics</li><li>Classifying Images with Deep Convolutional Neural Networks</li><li>Modeling Sequential Data Using Recurrent Neural Networks</li><li>GANs for Synthesizing New Data</li><li>RL for Decision Making in Complex Environments</li></ol>
You Look Like a Thing and I Love You: How Artificial Intelligence...
by Janelle Shane

Language

English

Pages

273

Publication Date

November 05, 2019

Product Description
Customer Reviews
<b>AS HEARD ON NPR'S "SCIENCE FRIDAY"</b><b><br /></b><b>Discover the book that Malcolm Gladwell, Susan Cain, Daniel Pink, and Adam Grant want you to read this year, an "accessible, informative, and hilarious" introduction to the weird and wonderful world of artificial intelligence (Ryan North).</b><br />"You look like a thing and I love you" is one of the best pickup lines ever... according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog <i>AI Weirdness</i>. She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans--all to understand the technology that governs so much of our daily lives.<br />We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI <i>really</i>... and how does it solve problems, understand humans, and even drive self-driving cars?<br />Shane delivers the answers to every AI question you've ever asked, and some you definitely haven't. Like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like? And is the world's best Halloween costume really "Vampire Hog Bride"?<br />In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail, and adapt--and how they reflect the best and worst of humanity.<br /><i>You Look Like a Thing and I Love You </i>is the perfect book for anyone curious about what the robots in our lives are thinking.<br /><b>"I can't think of a better way to learn about artificial intelligence, and I've never had so much fun along the way." - Adam Grant, <i>New York Times </i>bestselling author of <i>Originals</i></b><b><i><br /></i></b>
Code: The Hidden Language of Computer Hardware and Software (Deve...
by Charles Petzold

Language

English

Pages

400

Publication Date

October 11, 2000

Product Description
Customer Reviews
<p>What do flashlights, the British invasion, black cats, and seesaws have to do with computers? In CODE, they show us the ingenious ways we manipulate language and invent new means of communicating with each other. And through CODE, we see how this ingenuity and our very human compulsion to communicate have driven the technological innovations of the past two centuries. <br /> Using everyday objects and familiar language systems such as Braille and Morse code, author Charles Petzold weaves an illuminating narrative for anyone who’s ever wondered about the secret inner life of computers and other smart machines. <br /> It’s a cleverly illustrated and eminently comprehensible story—and along the way, you’ll discover you’ve gained a real context for understanding today’s world of PCs, digital media, and the Internet. No matter what your level of technical savvy, CODE will charm you—and perhaps even awaken the technophile within.
Monolith to Microservices: Evolutionary Patterns to Transform You...
by Sam Newman

Language

English

Pages

272

Publication Date

November 14, 2019

Product Description
Customer Reviews
<p>How do you detangle a monolithic system and migrate it to a microservice architecture? How do you do it while maintaining business-as-usual? As a companion to Sam Newman’s extremely popular <i>Building Microservices</i>, this new book details a proven method for transitioning an existing monolithic system to a microservice architecture.</p><p>With many illustrative examples, insightful migration patterns, and a bevy of practical advice to transition your monolith enterprise into a microservice operation, this practical guide covers multiple scenarios and strategies for a successful migration, from initial planning all the way through application and database decomposition. You’ll learn several tried and tested patterns and techniques that you can use as you migrate your existing architecture.</p><ul><li>Ideal for organizations looking to transition to microservices, rather than rebuild</li><li>Helps companies determine whether to migrate, when to migrate, and where to begin</li><li>Addresses communication, integration, and the migration of legacy systems</li><li>Discusses multiple migration patterns and where they apply</li><li>Provides database migration examples, along with synchronization strategies</li><li>Explores application decomposition, including several architectural refactoring patterns</li><li>Delves into details of database decomposition, including the impact of breaking referential and transactional integrity, new failure modes, and more</li></ul>

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