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Deep Learning with TensorFlow

You're reading from Deep Learning with TensorFlow Explore neural networks and build intelligent systems with Python

Product type Paperback

Published in Mar 2018

Publisher Packt

ISBN-13 9781788831109

Length 484 pages

Edition 2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Deep Learning

Authors (2):

Giancarlo Zaccone

Md. Rezaul Karim

View More author details

Table of Contents (13) Chapters

Preface

1. Getting Started with Deep Learning FREE CHAPTER

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

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Index

TensorFlow computational graph

When thinking of executing a TensorFlow program, we should be familiar with the concepts of graph creation and session execution. Basically, the first one is for building the model, and the second one is for feeding the data in and getting the results.

Interestingly, TensorFlow does everything on the C++ engine, which means not even a little multiplication or addition is executed in Python. Python is just a wrapper. Fundamentally, the TensorFlow C++ engine consists of the following two things:

Efficient implementations of operations, such as convolution, max pool, and sigmoid for a CNN for example
Derivatives of the forwarding mode operation

The TensorFlow lib is an extraordinary lib in terms of coding and it is not like conventional Python code (for example, you can write statements and they get executed). TensorFlow code consists of different operations. Even variable initialization is special in TensorFlow. When you are performing a complex operation with TensorFlow...

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Authors (2)

Zaccone

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).

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Karim

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.

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