Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

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  best-selling 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 Aurelien Geron 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 machine learning 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, and transformers 
• Use  TensorFlow and Keras to build and train neural nets for computer  vision, natural language processing, generative models, and deep  reinforcement learning 
• Train neural nets using multiple GPUs and deploy them at scale using Google's Vertex AI