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

Advancing Language Understanding and Generation with the Transformer Models

In the previous chapter, we focused on RNNs and used them to deal with sequence learning tasks. However, RNNs may easily suffer from the vanishing gradient problem. In this chapter, we will explore the Transformer neural network architecture, which is designed for sequence-to-sequence tasks and is particularly well suited for Natural Language Processing (NLP). The key innovation is the self-attention mechanism, allowing the model to weigh different parts of the input sequence differently, and enabling it to capture long-range dependencies more effectively than RNNs.

We will learn two cutting-edge models utilizing the Transformer architecture and delve into their practical applications, such as sentiment analysis and text generation. Expect enhanced performance on tasks previously covered in the preceding chapter.

We will cover the following topics in this chapter: