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Python Machine Learning By Example

Python Machine Learning By Example - Fourth Edition

By : Yuxi (Hayden) Liu
4.9 (8)
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Python Machine Learning By Example

Python Machine Learning By Example

4.9 (8)
By: Yuxi (Hayden) Liu

Overview of this book

The fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.
Table of Contents (18 chapters)
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16
Other Books You May Enjoy
17
Index

Making Decisions in Complex Environments with Reinforcement Learning

In the previous chapter, we focused on multimodal models for image and text co-learning. The last chapter of this book will be about reinforcement learning, which is the third type of machine learning task mentioned at the beginning of the book. You will see how learning from experience and learning by interacting with the environment differs from previously covered supervised and unsupervised learning.

We will cover the following topics in this chapter:

  • Setting up the working environment
  • Introducing reinforcement learning with examples
  • Solving the FrozenLake environment with dynamic programming
  • Performing Monte Carlo learning
  • Solving the Taxi problem with the Q-learning algorithm
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Python Machine Learning By Example
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