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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):

Zaccone

Karim

View More author details

Table of Contents (15) Chapters

Deep Learning with TensorFlow - Second Edition

Contributors

Preface

Other Books You May Enjoy

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

Index

Summary

In this chapter, we looked at some TensorFlow-based libraries for DL research and development. We introduced tf.estimator, which is a simplified interface for DL/ML, and is now part of TensorFlow and a high-level ML API that makes it easy to train, configure, and evaluate a variety of ML models. We used the estimator feature to implement a classifier for the Iris dataset.

We also had a look at the TFLearn library, which wraps a lot of TensorFlow APIs. In the example, we used TFLearn to estimate the chance of survival of passengers on the Titanic. To tackle this task, we built a DNN classifier.

Then, we introduced PrettyTensor, which allows TensorFlow operations to be wrapped to chain any number of layers. We implemented a convolutional model in the style of LeNet to quickly resolve the handwritten classification model.

Then we had a quick look at Keras, which is designed for minimalism and modularity, allowing the user to quickly define DL models. Using Keras, we have learned how to...

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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).

See other products by Zaccone

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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