You're reading from Python Machine Learning, Second Edition Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781787125933

Length 622 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Deep Learning

Authors (2):

Vahid Mirjalili

Sebastian Raschka

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Table of Contents (18) Chapters

Preface

1. Giving Computers the Ability to Learn from Data FREE CHAPTER

2. Training Simple Machine Learning Algorithms for Classification

3. A Tour of Machine Learning Classifiers Using scikit-learn

4. Building Good Training Sets – Data Preprocessing

5. Compressing Data via Dimensionality Reduction

6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning

7. Combining Different Models for Ensemble Learning

8. Applying Machine Learning to Sentiment Analysis

9. Embedding a Machine Learning Model into a Web Application

10. Predicting Continuous Target Variables with Regression Analysis

11. Working with Unlabeled Data – Clustering Analysis

12. Implementing a Multilayer Artificial Neural Network from Scratch

13. Parallelizing Neural Network Training with TensorFlow

14. Going Deeper – The Mechanics of TensorFlow

15. Classifying Images with Deep Convolutional Neural Networks

16. Modeling Sequential Data Using Recurrent Neural Networks

Index

Chapter 13. Parallelizing Neural Network Training with TensorFlow

In this chapter, we'll move on from the mathematical foundations of machine learning and deep learning to introducing TensorFlow. TensorFlow is one of the most popular deep learning libraries currently available, and it can let us implement neural networks much more efficiently than any of our previous NumPy implementations. In this chapter, we'll start using TensorFlow and see how it brings significant benefits to training performance.

This chapter begins the next stage of our journey into training machine learning and deep learning, and we'll explore the following topics:

How TensorFlow improves training performance
Working with TensorFlow to write optimized machine learning code
Using TensorFlow high-level APIs to build a multilayer neural network
Choosing activation functions for artificial neural networks
Introducing Keras, a high-level wrapper around TensorFlow, for implementing common deep learning...

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You're reading from Python Machine Learning, Second Edition Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Table of Contents (18) Chapters

Chapter 13. Parallelizing Neural Network Training with TensorFlow

Authors (2)

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Table of Contents (18) Chapters

Chapter 13. Parallelizing Neural Network Training with TensorFlow

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