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Deep Learning with TensorFlow

You're reading from Deep Learning with TensorFlow Explore neural networks with Python

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781786469786

Length 320 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Authors (4):

Zaccone

Milo

Karim

Menshawy

View More author details

Table of Contents (17) Chapters

Title Page

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Getting Started with Deep Learning FREE CHAPTER

2. First Look at TensorFlow

3. Using TensorFlow on a Feed-Forward Neural Network

4. TensorFlow on a Convolutional Neural Network

5. Optimizing TensorFlow Autoencoders

6. Recurrent Neural Networks

7. GPU Computing

8. Advanced TensorFlow Programming

9. Advanced Multimedia Programming with TensorFlow

10. Reinforcement Learning

Accelerated Linear Algebra

The Accelerated Linear Algebra (XLA) is a domain specific compiler developed by TensorFlow for optimizing its computations. By this, you will get improvements in speed, memory usage and even portability on mobile platforms.

Initially, you won't see much benefit from XLA because it's still experimental but you can try it by using the just-in-time compilation or ahead-of-time compilations.

First, we are going to briefly mention the key strengths of TensorFlow and see how TensorFlow team had the challenge to keep and increase these key strengths.

Key strengths of TensorFlow

The following are the key strengths of TensorFlow:

Flexible: This Flexibility is coming from TensorFlow's nature of being interpreted. Also, you can see it in the name that it uses a data flow programming mode. The way TensorFlow works, you give the program a graph then it tries to find a node in that graph that's ready to execute, subsequently, it fetches this node and runs it. After this sequence...

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Authors (4)

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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Milo

Milo

Contacted on 04/07/2017 - Brendan Rodrigues

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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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Menshawy

Menshawy

Ahmed Menshawy is a Research Engineer at the Trinity College Dublin, Ireland. He has more than 5 years of working experience in the area of ML and NLP. He holds an MSc in Advanced Computer Science. He started his Career as a Teaching Assistant at the Department of Computer Science, Helwan University, Cairo, Egypt. He taught several advanced ML and NLP courses such as ML, Image Processing, and so on. He was involved in implementing the state-of-the-art system for Arabic Text to Speech. He was the main ML specialist at the Industrial research and development lab at IST Networks, based in Egypt.

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