Updated in this second edition to cover TensorFlow 2.0, this book aims to show programmers who know little about machine learning how to use simple, efficient tools to implement programs capable of learning from data. Author Aurélien Géron uses two production-ready Python frameworks, Scikit-Learn and TensorFlow, to illustrate the concepts and tools for building intelligent systems. The book covers a range of techniques, starting with simple linear regression and progressing to deep neural networks.

<ASIN:1492032646>

Author: Aurélien Géron
Publisher: O'Reilly
Date: October 2019
Pages: 856
ISBN: 978-1492032649
Print: 1492032646
Kindle:B07XGF2G87
Audience: programmers interested in machine learning
Level: Intermediate
Category: Artificial Intelligence

• Explore the machine learning landscape, particularly neural nets
• Use Scikit-Learn to track an example machine-learning project end-to-end
• Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
• Use the TensorFlow library to build and train neural nets
• Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
• Learn techniques for training and scaling deep neural nets

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