You're reading from Python Machine Learning By Example Unlock machine learning best practices with real-world use cases

Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781835085622

Length 518 pages

Edition 4th Edition

Languages

Python

Tools

PyTorch

Concepts

Computer Vision

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (18) Chapters

Preface

1. Getting Started with Machine Learning and Python

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

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

4. Predicting Online Ad Click-Through with Logistic Regression

5. Predicting Stock Prices with Regression Algorithms

6. Predicting Stock Prices with Artificial Neural Networks

7. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

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

9. Recognizing Faces with Support Vector Machine

10. Machine Learning Best Practices

11. Categorizing Images of Clothing with Convolutional Neural Networks

12. Making Predictions with Sequences Using Recurrent Neural Networks

13. Advancing Language Understanding and Generation with the Transformer Models

14. Building an Image Search Engine Using CLIP: a Multimodal Approach

15. Making Decisions in Complex Environments with Reinforcement Learning

16. Other Books You May Enjoy

17. Index

Introducing OpenAI Gym and Gymnasium

OpenAI Gym was a toolkit for developing and comparing reinforcement learning algorithms. It provided a collection of environments, or “tasks,” in which reinforcement learning agents can interact and learn. These environments range from simple grid-world games to complex simulations of real-world scenarios, allowing researchers and developers to experiment with a wide variety of reinforcement learning algorithms. It was developed by OpenAI, focused on building safe and beneficial Artificial General Intelligence (AGI).

Some key features of OpenAI Gym included:

Environment interface: Gym provided a consistent interface for interacting with environments, allowing agents to observe states, take actions, and receive rewards (we will learn about these terms).
Extensive collection of environments: Gym offered a diverse set of environments, including classic control tasks, Atari games, robotics simulations, and more. This...