You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

4. Predicting Online Ad Click-Through with Tree-Based Algorithms

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. Index

Setting up the working environment

Let's get started with setting up the working environment, including PyTorch as the main framework, and OpenAI Gym, the toolkit that gives you a variety of environments to develop your learning algorithms on.

Installing PyTorch

PyTorch (https://pytorch.org/) is a trendy machine learning library developed on top of Torch (http://torch.ch/) by Facebook's AI lab. It provides powerful computational graphs and high compatibility to GPUs, as well as a simple and friendly interface. PyTorch is expanding quickly in academia and it has seen heavy adoption by more and more companies. The following chart (taken from http://horace.io/pytorch-vs-tensorflow/) shows the growth of PyTorch at top machine learning conferences: