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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

A general overview of TensorFlow

TensorFlow is an open source framework from Google for scientific and numerical computation using data flow graphs that stand for TensorFlow's execution model. The data flow graphs used in TensorFlow help ML experts to perform more advanced and intensive training on their data to develop DL and predictive analytics models.

As the name implies, TensorFlow includes operations that are performed by neural networks on multidimensional data arrays, that is, flow of tensors. Nodes in a flow graph correspond to mathematical operations, that is, addition, multiplication, matrix factorization, and so on; whereas, edges correspond to tensors that ensure communication between edges and nodes – that is, data flow and control flow. This way, TensorFlow provides some widely used and robustly implemented linear models and DL algorithms.

You can perform numerical computations on a CPU. However, with TensorFlow, it is also possible to distribute the training among...

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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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