Previous editions: 2019, 2017

The fundamentals of machine learning. The machine learning landscape ; End-to-end machine learning project ; Classification ; Training models ; Support vector machines ; Decision trees ; Ensemble learning and random forests ; Dimensionality reduction ; Unsupervised learning techniques -- Neural networks and deep learning. Introduction to artificial neural networks with Keras ; Training deep neural networks ; Custom models and training with TensorFlow ; Loading and preprocessing data with TensorFlow ; Deep computer vision using convolutional neural networks ; Processing sequences using RNNs and CNNs ; Natural language processing with RNNs and attention ; Autoencoders, GANs, and diffusion models ; Reinforcement learning ; Training and deploying TensorFlow models at scale

"Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started: Use Scikit-learn to track an example ML project end to end; Explore several models, including support vector machines, decision trees, random forests, and ensemble methods; Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection; Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers; Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning" --Back cover