You're reading from Python Machine Learning - Third Edition

Product typeBook

Published inDec 2019

Reading LevelExpert

PublisherPackt

ISBN-139781789955750

Edition3rd Edition

Languages

Python

Tools

TensorFlow Keras

Concepts

Machine Learning

Authors (2):

Sebastian Raschka

Vahid Mirjalili

View More author details

Summary

In this chapter, we covered TensorFlow's most essential and useful features. We started by discussing the migration from TensorFlow v1.x to v2. In particular, we used TensorFlow's dynamic computation graph approach, the so-called eager execution mode, which makes implementing computations more convenient compared to using static graphs. We also covered the semantics of defining TensorFlow Variable objects as model parameters, annotating Python functions using the tf.function decorator to improve computational efficiency via graph compilation.

After we considered the concept of computing partial derivatives and gradients of arbitrary functions, we covered the Keras API in more detail. It provides us with a user-friendly interface for building more complex deep NN models. Finally, we utilized TensorFlow's tf.estimator API to provide a consistent interface that is typically preferred in production environments. We concluded this chapter by converting a Keras...

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Python Machine Learning - Third Edition

Published in: Dec 2019Publisher: PacktISBN-13: 9781789955750

Authors (2)

Sebastian Raschka

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.
Read more about Sebastian Raschka

Vahid Mirjalili

Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.
Read more about Vahid Mirjalili

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You're reading from Python Machine Learning - Third Edition

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Authors (2)

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