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Authored by leading experts in the field, this authoritative text provides an in-depth exploration of the algorithms and mathematical models for modern natural language processing and speech recognition, and is continually updated to reflect the rapid advancements in the NLP domain.
Section: Theory of LLMs · Authored by leading experts in the field, this authoritative text provides an in-depth exploration of the algorithms and mathematical models for modern natural language processing and speech recognition, and is continually updated to reflect the rapid advancements in the NLP domain.
Section: Books · The continuously updated NLP reference by Dan Jurafsky and James Martin.
Section: Books · free, by Prof. Dan Jurafsy
Section: Machine Learning · by Daniel Jurafsky and James H. Martin
website with the book draft and Google Colabs of the book by Simon J.D. Prince
bt Tiago MOnteiro | A free FreeCodeCamp book teaching the math behind AI in plain English from an engineering point of view. It covers linear algebra, calculus, probability & statistics, and optimization theory with analogies, real-life applications, and Python code examples.
Learn the essentials of machine learning by completing a carefully designed set of real-world projects.
Mathematical foundations by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
This book serves as an introduction of text mining using the tidytext package and other tidy tools in R. Authors: Julia Silge and David Robinson.
This course provides a comprehensive insight into Deep Learning for NLP using PyTorch, emphasizing end-to-end neural models, eliminating the need for task-specific feature engineering, and equipping students with the skills to craft their own neural network solutions.
An online and print book introducing NLP concepts using NLTK. The book's authors also wrote the NLTK library.
Free-to-read online textbook by Christopher M. Bishop and Hugh Bishop, with a probability-based treatment of modern deep-learning models and methods.