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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In this book. subtitled "A Friendly Approach to Modern Functional Programming, Type Theory, and Artificial Intelligence", Anton Antich uses playful metaphors and examples to help teach Haskell through imagination, building on math without relying on imperative crutches or technical complexity. Readers will use math to build completely different Typed Functional patterns from the ground up and understand the link between building mathematics through yypes and constructing Haskell as a programming language. 

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This book introduces mathematical techniques, key algorithms, and Python implementations for building machine learning models for unannotated data. It bridges the gap between complex math and practical Python implementations, covering end-to-end model development all the way through to production deployment. Vaibhav Verdhan introduces hands-off and unsupervised machine learning approaches that can still untangle raw, real-world datasets and support sound strategic business decisions.

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Subtitled "A mainframe in my living room", this book is about the PiDP-10 kit and the three operating systems that you can run on it. Andrew Barron acknowledges that very few people have the room, the money, or the time to restore a full-size PDP-10 as they were large installations that required a big climate-controlled room. However, he points out that due to the work by Bob Supnik, the original developer of the SimH emulator, enthusiasts can download an emulator onto a Raspberry Pi and run an accurate simulation of the DEC PDP-10 system.

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In this book Susanne Kaiser looks at how to design and build adaptive systems that can handle change. By combining Wardley Mapping, Domain-Driven Design, and Team Topologies, this book offers a comprehensive toolset for organizations to anticipate change. Drawing from historical examples of companies that failed to adapt, Kaiser emphasizes that optimization requires treating organizations as socio-technical systems where social and technical aspects are aligned and designed together.

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