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Python Machine Learning

You're reading from Python Machine Learning Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2

Product type Paperback

Published in Dec 2019

Publisher Packt

ISBN-13 9781789955750

Length 772 pages

Edition 3rd Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Authors (2):

Vahid Mirjalili

Sebastian Raschka

View More author details

Table of Contents (21) Chapters

Preface

1. Giving Computers the Ability to Learn from Data

2. Training Simple Machine Learning Algorithms for Classification FREE CHAPTER

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

4. Building Good Training Datasets – 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

17. Generative Adversarial Networks for Synthesizing New Data

18. Reinforcement Learning for Decision Making in Complex Environments

19. Other Books You May Enjoy

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20. Index

TensorFlow's computation graphs: migrating to TensorFlow v2

TensorFlow performs its computations based on a directed acyclic graph (DAG). In TensorFlow v1.x, such graphs could be explicitly defined in the low-level API, although this was not trivial for large and complex models. In this section, we will see how these graphs can be defined for a simple arithmetic computation. Then, we will see how to migrate a graph to TensorFlow v2, the eager execution and dynamic graph paradigm, as well as the function decoration for faster computations.

Understanding computation graphs

TensorFlow relies on building a computation graph at its core, and it uses this computation graph to derive relationships between tensors from the input all the way to the output. Let's say that we have rank 0 (scalar) tensors a, b, and c and we want to evaluate . This evaluation can be represented as a computation graph, as shown in the following figure:

As you can see, the computation graph...

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Authors (2)

Sebastian Raschka

Sebastian Raschka

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.

See other products by Sebastian Raschka

Vahid Mirjalili

Vahid Mirjalili

Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.

See other products by Vahid Mirjalili

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