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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

What's new from TensorFlow v1.6 forwards?

In 2015, Google made TensorFlow open source, including all of its reference implementation. All of the source code was made available on GitHub under the Apache 2.0 license. Since then, TensorFlow has been widely adopted in academia and industrial research, and the most stable version, 1.6, has recently been released with a unified API.

It is important to note that the APIs in TensorFlow 1.6 (and higher) are not all backward compatible for pre v1.5 code. This means that some programs that worked on pre v1.5 will not necessarily work on TensorFlow 1.6.

Now let us see the new and exciting features that TensorFlow v1.6 has.

Nvidia GPU support optimized

From TensorFlow v1.5, prebuilt binaries are now built against CUDA 9.0 and cuDNN 7. However, from v1.6's release, TensorFlow prebuilt binaries use AVX instructions, which may break TensorFlow on older CPUs. Nevertheless, since v1.5, an added support for CUDA on NVIDIA Tegra devices has been available.

Introducing...

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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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