Topic: Computer Science

Pro Git, 2nd Edition is your fully-updated guide to Git and its usage in the modern world. Git has come a long way since it was first developed by Linus Torvalds...

This textbook serves as a gentle introduction for undergraduates to theoretical concepts in data structures and algorithms in computer science while providing coverage of practical implementation (coding) issues. The field...

How we can enact meaningful change in computing to meet the urgent need for sustainability and justice. The deep entanglement of information technology with our societies has raised hope for...

How Bulgaria transformed the computer industry behind the Iron Curtain—and the consequences of that transformation for a society that dreamt of a brighter future. Bulgaria in 1963 was a communist...

A completely revised edition, offering new design recipes for interactive programs and support for images as plain values, testing, event-driven programming, and even distributed programming. This introduction to programming places...

As Python continues to grow in popularity, projects are becoming larger and more complex. Many Python developers are taking an interest in high-level software design patterns such as hexagonal/clean architecture,...

By taking you through the development of a real web application from beginning to end, the second edition of this hands-on guide demonstrates the practical advantages of test-driven development (TDD)...

Essays on the challenges and risks of designing algorithms and platforms for children, with an emphasis on algorithmic justice, learning, and equity. One in three Internet users worldwide is a...

Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency....

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to...

This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers...

Interpretable Machine Learning is a comprehensive guide to making machine learning models interpretable "Pretty convinced this is the best book out there on the subject" – Brian Lewis, Data Scientist...