A Discipline of Programming


Edsger W. Dijkstra - 1976
    

The Google Story: Inside the Hottest Business, Media and Technology Success of Our Time


David A. Vise - 2005
    The Google Story takes you deep inside the company's wild ride from an idea that struggled for funding in 1998 to a firm that rakes in billions in profits, making Brin and Page the wealthiest young men in America. Based on scrupulous research and extraordinary access to Google, this fast-moving narrative reveals how an unorthodox management style and culture of innovation enabled a search engine to shake up Madison Avenue and Wall Street, scoop up YouTube, and battle Microsoft at every turn. Not afraid of controversy, Google is expanding in Communist China and quietly working on a searchable genetic database, initiatives that test the founders' guiding mantra: DON'T BE EVIL.

Reversing: Secrets of Reverse Engineering


Eldad Eilam - 2005
    The book is broken into two parts, the first deals with security-related reverse engineering and the second explores the more practical aspects of reverse engineering. In addition, the author explains how to reverse engineer a third-party software library to improve interfacing and how to reverse engineer a competitor's software to build a better product. * The first popular book to show how software reverse engineering can help defend against security threats, speed up development, and unlock the secrets of competitive products * Helps developers plug security holes by demonstrating how hackers exploit reverse engineering techniques to crack copy-protection schemes and identify software targets for viruses and other malware * Offers a primer on advanced reverse-engineering, delving into disassembly-code-level reverse engineering-and explaining how to decipher assembly language

Hands-On Machine Learning with Scikit-Learn and TensorFlow


Aurélien Géron - 2017
    Now that machine learning is thriving, 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.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 how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

Digital Gold: Bitcoin and the Inside Story of the Misfits and Millionaires Trying to Reinvent Money


Nathaniel Popper - 2015
    Believers from Beijing to Buenos Aires see the potential for a financial system free from banks and governments. More than just a tech industry fad, Bitcoin has threatened to decentralize some of society’s most basic institutions.An unusual tale of group invention, Digital Gold charts the rise of the Bitcoin technology through the eyes of the movement’s colorful central characters, including an Argentinian millionaire, a Chinese entrepreneur, Tyler and Cameron Winklevoss, and Bitcoin’s elusive creator, Satoshi Nakamoto. Already, Bitcoin has led to untold riches for some, and prison terms for others.

Sams Teach Yourself SQL™ in 10 Minutes


Ben Forta - 1999
    It also covers MySQL, and PostgreSQL. It contains examples which have been tested against each SQL platform, with incompatibilities or platform distinctives called out and explained.

Learning Web Design: A Beginner's Guide to Html, Css, Javascript, and Web Graphics


Jennifer Niederst Robbins - 2001
    You’ll begin at square one, learning how the Web and web pages work, and then steadily build from there. By the end of the book, you’ll have the skills to create a simple site with multi-column pages that adapt for mobile devices.Learn how to use the latest techniques, best practices, and current web standards—including HTML5 and CSS3. Each chapter provides exercises to help you to learn various techniques, and short quizzes to make sure you understand key concepts.This thoroughly revised edition is ideal for students and professionals of all backgrounds and skill levels, whether you’re a beginner or brushing up on existing skills.Build HTML pages with text, links, images, tables, and formsUse style sheets (CSS) for colors, backgrounds, formatting text, page layout, and even simple animation effectsLearn about the new HTML5 elements, APIs, and CSS3 properties that are changing what you can do with web pagesMake your pages display well on mobile devices by creating a responsive web designLearn how JavaScript works—and why the language is so important in web designCreate and optimize web graphics so they’ll download as quickly as possible

Software Engineering (International Computer Science Series)


Ian Sommerville - 1982
    Restructured into six parts, this new edition covers a wide spectrum of software processes from initial requirements solicitation through design and development.

Deep Learning with Python


François Chollet - 2017
    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.