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Deep Learning with TensorFlow. - Second Edition

You're reading from Deep Learning with TensorFlow. - Second Edition

Product type Book

Published in Mar 2018

Publisher Packt

ISBN-13 9781788831109

Pages 484 pages

Edition 2nd Edition

Languages

Concepts

Authors (2):

Giancarlo Zaccone

Md. Rezaul Karim

View More author details

Table of Contents (15) Chapters

Deep Learning with TensorFlow - Second Edition

Contributors

Preface

Other Books You May Enjoy

1. Getting Started with Deep Learning

2. A First Look at TensorFlow

3. Feed-Forward Neural Networks with TensorFlow

4. Convolutional Neural Networks

5. Optimizing TensorFlow Autoencoders

6. Recurrent Neural Networks

7. Heterogeneous and Distributed Computing

8. Advanced TensorFlow Programming

9. Recommendation Systems Using Factorization Machines

10. Reinforcement Learning

Index

Summary

TensorFlow is designed to make predictive analytics through ML and DL easy for everyone, but using it does require a decent understanding of some general principles and algorithms. The latest release of TensorFlow comes with lots of exciting new features, so we have tried to cover them so that you can use them with ease. In summary, here is a brief recap of the key concepts of TensorFlow that have been explained in this chapter:

Graph: Each TensorFlow computation can be represented as a data flow graph, where each graph is built as a set of operation objects. There are three core graph data structures: tf.Graph (https://www.tensorflow.org/api_docs/python/tf/Graph), tf.Operation (https://www.tensorflow.org/api_docs/python/tf/Operation), and tf.Tensor (https://www.tensorflow.org/api_docs/python/tf/Tensor).
Operation: A graph node takes one or more tensors as input and produces one or more tensors as output. A node can be represented by an operation object for performing computational...

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Authors (2)

Giancarlo Zaccone

Giancarlo Zaccone has over fifteen years' experience of managing research projects in the scientific and industrial domains. He is a software and systems engineer at the European Space Agency (ESTEC), where he mainly deals with the cybersecurity of satellite navigation systems. Giancarlo holds a master's degree in physics and an advanced master's degree in scientific computing. Giancarlo has already authored the following titles, available from Packt: Python Parallel Programming Cookbook (First Edition), Getting Started with TensorFlow, Deep Learning with TensorFlow (First Edition), and Deep Learning with TensorFlow (Second Edition).

See other products by Giancarlo Zaccone

Md. Rezaul Karim

Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.

See other products by Md. Rezaul Karim

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