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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

TensorFlow and Keras

In this section we are going to address a very important feature for all data scientists and machine learning enthusiasts which is the integration of TensorFlow and Keras. Having this feature on board, you will be able to build a very complex deep learning systems with very few lines of code.

Figure 5: TensorFlow and Keras Integration (Source: https://blog.keras.io/img/keras-tensorflow-logo.jpg)

What is Keras?

Keras is an API that makes using and building deep learning models easier and faster. So it's a deep learning toolbox that's all about:

Ease of use
Reducing complexity
Reducing cognitive load

And by making deep learning easier to use what happens is that you are making it accessible to more people. So the key design concept behind Keras is that to put deep learning into the hands of everyone.

So Keras is more like an API that has several different implementations. There's the Theano implementation which was originally released with Keras and you also have the TensorFlow...

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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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