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The Self-Taught Programmer: The Definitive Guide to Programming P...
by Cory Althoff

Language

English

Pages

301

Publication Date

December 26, 2016

Product Description
Customer Reviews
I am a self-taught programmer. After a year of self-study, I learned to program well enough to land a job as a software engineer II at eBay. Once I got there, I realized I was severely under-prepared. I was overwhelmed by the amount of things I needed to know but hadn't learned yet. My journey learning to program, and my experience at my first job as a software engineer were the inspiration for this book. <br /><br />This book is not just about learning to program; although you will learn to code. If you want to program professionally, it is not enough to learn to code; that is why, in addition to helping you learn to program, I also cover the rest of the things you need to know to program professionally that classes and books don't teach you. "The Self-taught Programmer" is a roadmap, a guide to take you from writing your first Python program, to passing your first technical interview. I divided the book into six sections: <br /><br />1. Learn to program in Python 3 and build your first program. <br />2. Learn Object-oriented programming and create a powerful Python program to get you hooked. <br />3. Learn to use tools like Git, Bash, regular expressions and databases. Then use your new coding skills to build a web scraper. <br />4. Study Computer Science fundamentals like data structures and algorithms. <br />5. Finish with tips for working with a team and landing a programming job. <br /><br />You CAN learn to program professionally. The path is there. Will you take it?
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Conce...
by Aurélien Géron

Language

English

Pages

574

Publication Date

March 13, 2017

Product Description
Customer Reviews
<div><p><b>Graphics in this book are printed in black and white</b>.</p><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><li>Apply practical code examples without acquiring excessive machine learning theory or algorithm details</li></ul></div>
Data Science (The MIT Press Essential Knowledge series)
by , Brendan Tierney

Language

English

Pages

282

Publication Date

April 06, 2018

Product Description
Customer Reviews
<P><B>A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.</B></P><P>The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.</P><P>It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.</P>
CompTIA A+ Certification All-in-One Exam Guide, Ninth Edition (Ex...
by Mike Meyers

Language

English

Pages

1508

Publication Date

January 07, 2016

Product Description
Customer Reviews
<h4>This bestselling on-the-job reference and test preparation guide has been fully revised for the new 2015 CompTIA exam objectives for exams 901 & 902</h4> <p>Written by the leading authority on CompTIA A+ certification and training, this self-study set has been thoroughly updated to cover 100% of the topics covered on the latest edition of the exam. New topics include managing and maintaining cellular devices, including tablets; configuring operating systems, including Windows 8, Android, and iOS; and enhanced, mobile-centered security and troubleshooting procedures. The <em>All-in-One Exam Guide</em> enables you to take the test with complete confidence. It also serves as a practical reference for IT support and technical personnel.</p> <p><strong>Bonus electronic content includes:</strong></p> <ul><li>Practice exams with hundreds of accurate questions</li> <li>More than an hour of video training featuring Mike Meyers</li> <li>Performance-based simulations that prepare you for the performance-based questions on the exam</li> <li>A collection of Mike's favorite free PC tools</li> </ul> <p><strong>Key Features include:</strong></p> <ul><li>Written with the “in the trenches” voice and clarity Mike Meyers is known for</li> <li>Features pre-assessment tests, exam tips, and “Try This!” sections to reinforce difficult topics</li> <li>Includes a coupon for 10% off of the exam fee, a $37 value</li> </ul>
Weapons of Math Destruction: How Big Data Increases Inequality an...
by Cathy O'Neil

Language

English

Pages

254

Publication Date

September 06, 2016

Product Description
Customer Reviews
<b><i>Longlisted for the National Book Award<br /></i>New York Times <i>Bestseller</i><br /><br />A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life — and threaten to rip apart our social fabric</b><br /><br />We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we get a car loan, how much we pay for health insurance—are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated.<br /><br />But as Cathy O’Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (by virtue of his zip code), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy.” Welcome to the dark side of Big Data.<br /><br />Tracing the arc of a person’s life, O’Neil exposes the black box models that shape our future, both as individuals and as a society. These “weapons of math destruction” score teachers and students, sort résumés, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health.<br /><br />O’Neil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it’s up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.<br /><br />— Longlist for National Book Award (Non-Fiction)<br />— Goodreads, semi-finalist for the 2016 Goodreads Choice Awards (Science and Technology)<br />— Kirkus, Best Books of 2016<br />— <i>New York Times,</i> 100 Notable Books of 2016 (Non-Fiction)<br />— <i>The Guardian</i>, Best Books of 2016<br />— WBUR's "On Point," Best Books of 2016: Staff Picks<br />— Boston Globe, Best Books of 2016, Non-Fiction
macOS High Sierra: The Missing Manual: The book that should have ...
by David Pogue

Language

English

Pages

888

Publication Date

January 31, 2018

Product Description
Customer Reviews
<div><p>With High Sierra, Apple has taken its macOS operating system to new heights. From Apple’s efficient new file system to improved video streaming and support for virtual reality, this latest macOS version provides features improve your experience. And once again, David Pogue brings his humor and expertise to the #1 bestselling Mac book.</p><p>Whether you’re a developer or a home-user, this guide offers a wealth of detail on Apple’s macOS 10.13 operating system, this new edition covers everything High Sierra has to offer.</p><ul><li><b>Perfect for newcomers.</b> Get crystal-clear, jargon-free introductions to the best and brightest macOS apps, including Siri, Safari, iCloud, FaceTime, and AirDrop.</li><li><b>Get the whole picture.</b> Learn more efficient ways to navigate, organize, and edit your photos with new features in the Photos app.</li><li><b>Go in-depth.</b> Take advantage of Apple’s new graphics technology, and its support for virtual reality.<l><b>Gain real insight.</b> David Pogue doesn't just tell you how to use macOS features. You’ll also learn <i>why</i> to use them—and how to avoid common pitfalls.i</l></li></ul></div>
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.
Numsense! Data Science for the Layman: No Math Added
by , Kenneth Soo

Language

English

Pages

147

Publication Date

February 03, 2017

Product Description
Customer Reviews
<b> Used in Stanford's CS102 Big Data course.</b><br /><br />Want to get started on data science?<br />Our promise: no math added.<br /><br />This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations and visuals.<br /><br />Popular concepts covered include:<br /><br />- A/B Testing<br />- Anomaly Detection<br />- Association Rules<br />- Clustering<br />- Decision Trees and Random Forests<br />- Regression Analysis<br />- Social Network Analysis<br />- Neural Networks<br /><br />Features:<br /><br />- Intuitive explanations and visuals<br />- Real-world applications to illustrate each algorithm<br />- Point summaries at the end of each chapter<br />- Reference sheets comparing the pros and cons of algorithms<br />- Glossary list of commonly-used terms<br /><br />With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.
The Master Algorithm: How the Quest for the Ultimate Learning Mac...
by Pedro Domingos

Language

English

Pages

338

Publication Date

September 22, 2015

Product Description
Customer Reviews
<div><b>"Wonderfully erudite, humorous, and easy to read." --<i>KDNuggets</i></b></div><div><br /></div><div>In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In <i>The Master Algorithm</i>, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.<br /></div>
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 style="margin: 0px;"> </p>

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Enter the Kind Reader Monthly Drawing

Kind Reader Monthly Drawing (March 2017)

Congratulations to February 2017's winner Henry H. of New York, USA.

There's a daily limit of 3 free e-books that can be downloaded at KindReader.com