Books to read

Generative Deep Learning

Author: David Foster This book provides a practical guide to creating generative models using TensorFlow and Keras, covering various architectures like VAEs, GANs, and Transformers.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Author: Aurelien Geron A comprehensive resource that covers a wide range of machine learning techniques, including deep learning and NLP, with practical examples in Python.

Natural Language Processing with Python

Authors: Steven Bird, Ewan Klein, and Edward Loper This book offers an introduction to NLP using Python, guiding readers through building working NLP tools.

Artificial Intelligence: A Modern Approach

Authors: Stuart Russell and Peter Norvig A widely-used textbook that provides a comprehensive introduction to the theory and practice of AI, including machine learning and NLP.

Deep Learning

Authors: Ian Goodfellow, Yoshua Bengio, and Aaron Courville This book offers an in-depth exploration of deep learning techniques, essential for understanding advanced machine learning and generative models.

Generative AI with Python and TensorFlow 2

Authors: Joseph Babcock and Raghav Bali This book guides you through building generative models, including VAEs and GANs, using Python and TensorFlow 2.

Introduction to Natural Language Processing

Author: Jacob Eisenstein An accessible introduction to NLP, covering both the theoretical foundations and practical applications.

The Hundred-Page Machine Learning Book

Author: Andriy Burkov A concise yet comprehensive guide to machine learning concepts, suitable for both beginners and experienced practitioners.

Natural Language Processing with PyTorch

Authors: Delip Rao and Brian McMahan This book provides practical guidance on building NLP applications using the PyTorch library.

Generative AI with LangChain

Author: Ben Auffarth A comprehensive guide to building large language model applications using the LangChain framework.