Deep learning has boosted the entire field of machine learning, and makes it easier for programmers who aren't machine learning experts to use simple, efficient tools to implement programs capable of learning from data. In this practical book author Aurelien Geron shows you how, by using concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn and TensorFlow.

<ASIN:1491962291>

You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.

Author: Aurelien Geron
Publisher: O'Reilly
Date: March 2017
Pages: 566
ISBN: 978-1491962299
Print: 1491962291
Kindle: B06XNKV5TS
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
• Apply practical code examples without acquiring excessive machine learning theory or algorithm details

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