Legally Free Computer Books
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Understanding Machine Learning: From Theory to Algorithms provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. …
Algorithms for Decision Making is a broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. …
Immerse yourself in Dive into Deep Learning, an engaging and accessible resource. Tailored for engineers, scientists, and students, it strikes a perfect balance between simplicity and technical rigor. You don’t need prior machine learning expertise; every concept is introduced anew. The appendix offers a math refresher, while practical runnable code instances foster intuitive understanding. Experience deep learning’s potential firsthand as you engage with this reader-friendly guide. …
In Robotic Process Automation Succinctly, author Ed Freitas will show readers how to use nonproprietary and open-source software to write RPA scripts using Python and some RPA techniques to automate some common and repetitive business tasks. …
In Neural Networks with JavaScript Succinctly, Author James McCaffrey leads you through the fundamental concepts of neural networks, including their architecture, input-output, tanh and softmax activation, back-propagation, error and accuracy, normalization and encoding, and model interpretation. …
Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book Text Mining with R, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. …
The Hundred-Page Machine Learning Book can be read during a week. During that week, you will learn almost everything the modern machine learning has to offer. The author and other practitioners have spent years learning these concepts. …
Neural networks are a powerful tool for developers, but harnessing them can be a challenge. With Keras Succinctly, author James McCaffrey introduces Keras, an open-source, neural network library designed specifically to make working with backend neural network tools easier. …
The purpose of this free online book, Neural Networks and Deep Learning is to help you master the core concepts of neural networks, including modern techniques for deep learning. After working through the book you will have written code that uses neural networks and deep learning to solve complex pattern recognition problems. And you will have a foundation to use neural networks and deep learning to attack problems of your own devising. …
Support Vector Machines (SVMs) are some of the most performant off-the-shelf, supervised machine-learning algorithms. In Support Vector Machines Succinctly, author Alexandre Kowalczyk guides readers through the building blocks of SVMs, from basic concepts to crucial problem-solving algorithms. He also includes numerous code examples and a lengthy bibliography for further study. By the end of the book, SVMs should be an important tool in the reader’s machine-learning toolbox. …