TensorFlow for Deep Learning
From Linear Regression to Reinforcement Learning
Publisher: O'Reilly Media
Final Release Date: March 2017
Pages: 300

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Learn how to solve challenging machine learning problems with Tensorflow, Google’s revolutionary new system for deep learning. If you have some background with basic linear algebra and calculus, this practical book shows you how to build—and when to use—deep learning architectures. You’ll learn how to design systems capable of detecting objects in images, understanding human speech, analyzing video, and predicting the properties of potential medicines.

TensorFlow for Deep Learning teaches concepts through practical examples and builds understanding of deep learning foundations from the ground up. It’s ideal for practicing developers comfortable with designing software systems, but not necessarily with creating learning systems. This book is also useful for scientists and other professionals who are comfortable with scripting, but not necessarily with designing learning algorithms.

  • Gain in-depth knowledge of the TensorFlow API and primitives.
  • Understand how to train and tune machine learning systems with TensorFlow on large datasets.
  • Learn how to use TensorFlow with convolutional networks, recurrent networks, LSTMs, and reinforcement learning.
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February 2018 (est.)