This is the third volume of Randall Hyde's Write Great Code series, this one concentrating on software engineering. It offers in-depth coverage of everything from development methodologies and strategic productivity to object-oriented design requirements and system documentation. The book covers the skills, attitudes, and ethics of quality software development and how to apply engineering principles to programming. Hyde lays out the rules, and when to break them, along with insights into best practices.

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The first edition of this book was honored as an ACM Notable Book. This new edition retains the same presentation, but has been upgraded to Python 3, and there is a new section on neural network styles. Cristina Lopes uses a simple computational task (term frequency) to illustrate different programming styles and how they can be used to write programs and design systems.

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George Dyson, the author of books such as Darwin Among the Machines and Turing’s Cathedral that look at the expansion of the digital age, has written a new book looking at anti-technologists and what they can tell us about the future. In Analogia, Dyson looks at historical figures from Native American leader Geronimo to physicist Leo Szilard who have either fought against technology or put forward alternative views, and considers a world driven by a generation of machines whose powers are beyond programmable control.

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This book shows how to use PyTorch for deep learning projects.  Authors.Eli Stevens, Luca Antiga and Thomas Viehmann show how to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. After covering the basics, the book moves on to larger projects, and the centerpiece of the book is a neural network designed for cancer detection.

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This book aims to offer an accessible introduction to algorithms, explaining not just what they are but how they work, with examples from a wide range of application areas. Arguing that every educated person today needs to have some understanding of algorithms and what they do, in this volume in the MIT Press Essential Knowledge series, Panos Louridas offers an introduction to algorithms that is accessible to the nonspecialist reader.

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As indicated by its subtitle, "Meditations on Python, mathematics, science, engineering, and design", this book is a collection of ten essays in which author Joel Grus explores core Python concepts, software design and testing, mathematics, and deep learning using the children's game Fizz Buzz as the guiding example. Each essay contains code that implements a different solution of Fizz Buzz. In each case the requirement is to print the numbers from 1 to 100, except that if the number is divisible by 3, print "fizz"; if the number is divisible by 5, print "buzz"; and if the number is divisible by 15, print "fizzbuzz".

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This book shows how to implement a series of best practices and design patterns to help create efficient and robust Node.js applications. Authors Mario Casciaro and Luciano Mammino kick off by exploring the basics of Node.js, analyzing its asynchronous event driven architecture and its fundamental design patterns. They then show how to build asynchronous control flow patterns with callbacks, promises and async/await.

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Take notice of this book's subtitle -The Making and Maintenance of Open Source Software. It's an inside look at modern open source software development, its evolution over the last two decades, and its ramifications for an internet reorienting itself around individual creators. Nadia Eghbal, who interviewed hundreds of developers while working to improve their experience at GitHub, argues that modern open source offers us a model through which to understand the challenges faced by online creators.

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In this book, subtitled "Ancient Wisdom for Cybersecurity Defense", Information Security professional Ken May takes the reader through choice bits of wisdom in the most lauded writings about warfare, political intrigue, martial arts, history, and strategy, and shows how it can pertain to protecting modern data systems. From Sun Tzu to Machiavelli, from Thucydides to Musashi, the tools and techniques may change, but the primary principles remain the same. This edition includes original language source texts, when available.

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This book introduces C and the C Standard Library, showing how to debug, test, and analyze C programs with concise explanations of C language constructs and behaviors, while addressing best practices, common errors, and open debates in the C community. Author Robert C. Seacord is an expert on the C Standards committee, and the book covers the modern C17 Standard as well as potential C2x features.

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This book describes some of the popular software application tools used in data science along with the processes for downloading and best using them. Author Christopher Greco considers data analysis using Microsoft Excel, KNIME, R, and the OpenOffice spreadsheet. Each of these tools are used to apply statistical concepts including confidence intervals, normal distribution, T-Tests, linear regression, histograms, and geographic analysis using real data from Federal Government sources.

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Algorithms for NP-Hard Problems is the fourth book in a series that provides an accessible, no-nonsense, and programming language-agnostic introduction to algorithms. The book includes hints or solutions to all quizzes and problems, and is accompanied by a series of YouTube videos by the author Tim Roughgarden, a Professor of Computer Science at Columbia University and well known to us for the Coursera Algorithms Specialization from when he was at Stanford University. Part 4, both of the Coursera series of courses and the set of books, covers algorithmic tools for tackling NP-hard problems (heuristic algorithms, local search, dynamic programming, MIP and SAT solvers) and techniques for quickly recognizing NP-hard problems in the wild.

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