This book covers advanced data structures and algorithms, exploring the efficiency of algorithms, and of data structures such as B-trees, bit vectors, and Bloom filters. Jay Wengrow shows how to use techniques like caching, randomization, and fingerprinting, using simple language, clear diagrams, and practice exercises and solutions. This Python edition uses Python exclusively for all code examples, exercises, and solutions.

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In this book, David Kopec explores the deeper principles behind code, such as how programming languages are implemented, what does an interpreter really do, and how does the microprocessor execute instructions at a fundamental level. The book is aimed at experienced Python programmers who want to fill in those gaps; not through abstract lectures, but through carefully designed projects that bring core CS concepts to life.

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In this book Daniel Temkin challenges conventional definitions of language, code, and computer, showing the potential of esolangs—or esoteric programming languages—as pure idea art. The languages in this volume ask programmers to write code in the form of prayer to the Greek gods, or as a pattern of empty folders, or to type code in tandem with another programmer, each with one hand on the keyboard, their rhythm and synchrony signifying computer action. 

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In this book Ole Olesen-Bagneux of Actian shows how to masterfully manage metadata repositories by properly coordinating them, arguing that until now, most repositories have been implemented in isolation from one another, but that practice lies at the core of problems with data management in many companies today. Better management requires a data discovery team to increase insights for all key players in enterprise data management, from the CIO and CDO to enterprise and data architects. Coordinating these repositories will help you and your organization democratize data and excel at data management.

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If you’re comfortable in Excel, but have hit a wall - slow files, broken formulas, hours spent on repetitive tasks - this book  offers a way forward. Tracy Stephens shows you how to take the work you already do in spreadsheets and make it faster, smarter, and more powerful with Python. The book starts from setting up your environment and getting comfortable with Python through short, Excel-inspired exercises. From there, readers gradually move into writing scripts that automate manual work, structure their data, and generate consistent results. No prior programming knowledge is required.

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This book is a guide to identifying, approaching, and triumphing over tasks beyond just laying down words as well as finding the power and joy in writing for video games. Richard Dansky looks at how to navigate the choppy waters of building schedules, interfacing with other team members, getting actionable feedback, and putting yourself in a position to do your best work without killing yourself.

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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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This book by Jens Gustedt, a member of the ISO C standards committee, gets readers up to speed with C23. The book is a fast-paced introduction to the C language, with special attention on its most modern features. It starts with a quick review of structure, grammar, and execution and then progresses quickly to control structures, data types, operators, and other core language features. Fully revised for C23, this expanded Third Edition covers compound expressions and lambdas, new insights into approaching program failure, and how to transition smoothly to C23.

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This book looks at how America designed and built the fastest chips and maintained its lead as the #1 superpower. Now, America's edge is slipping, undermined by competitors in Taiwan, Korea, Europe, and, above all, China. Today, as Chris Miller reveals, China, which spends more money each year importing chips than it spends importing oil, is pouring billions into a chip-building initiative to catch up to the US. At stake is America's military superiority and economic prosperity.

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How do you know what might have happened, had you done things differently? I this book, Robert Osazuwa Ness gives insights on how to make predictions and control outcomes based on causal relationships instead of pure correlation, so you can make precise and timely interventions. Ness' clear, code-first approach explains essential details of causal machine learning that are hidden in academic papers, and provides a practical introduction to building AI models that can reason about causality.

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In this book Anil Ananthaswamy explains the fundamental math behind machine learning-powered AI, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. Ananthaswamy goes on to suggest intriguing links between artificial and natural intelligence. Might the same math underpin them both? He also investigates the transformer architecture that makes large language models like ChatGPT possible and points to groundbreaking future directions enabled by the technology.

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From the creator of Dilbert, this is an updated second edition of Scott Adam's guidebook to spotting and avoiding loserthink - the  sneaky mental habits trapping victims in their own bubbles of reality. The premise of the book is that if you've been on social media lately, or turned on your TV, you may have noticed a lot of dumb ideas floating around, such as "We can tell the difference between evidence and coincidences," and "The simplest explanation is usually true."

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