You're reading from Machine Learning with PyTorch and Scikit-Learn Develop machine learning and deep learning models with Python

Product type Paperback

Published in Feb 2022

Publisher Packt

ISBN-13 9781801819312

Length 774 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Deep Learning

Authors (3):

Sebastian Raschka

Yuxi (Hayden) Liu

Vahid Mirjalili

View More author details

Table of Contents (22) Chapters

Preface

1. Giving Computers the Ability to Learn from Data FREE CHAPTER

2. Training Simple Machine Learning Algorithms for Classification

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. Predicting Continuous Target Variables with Regression Analysis

10. Working with Unlabeled Data – Clustering Analysis

11. Implementing a Multilayer Artificial Neural Network from Scratch

12. Parallelizing Neural Network Training with PyTorch

13. Going Deeper – The Mechanics of PyTorch

14. Classifying Images with Deep Convolutional Neural Networks

15. Modeling Sequential Data Using Recurrent Neural Networks

16. Transformers – Improving Natural Language Processing with Attention Mechanisms

17. Generative Adversarial Networks for Synthesizing New Data

18. Graph Neural Networks for Capturing Dependencies in Graph Structured Data

19. Reinforcement Learning for Decision Making in Complex Environments

20. Other Books You May Enjoy

21. Index

Summary

In this chapter, we introduced a whole new model architecture for natural language processing, the transformer architecture. The transformer architecture is built on a concept called self-attention, and we started introducing this concept step by step. First, we looked at an RNN outfitted with attention in order to improve its translation capabilities for long sentences. Then, we gently introduced the concept of self-attention and explained how it is used in the multi-head attention module within the transformer.

Many different derivatives of the transformer architecture have emerged and evolved since the original transformer was published in 2017. In this chapter, we focused on a selection of some of the most popular ones: the GPT model family, BERT, and BART. GPT is a unidirectional model that is particularly good at generating new text. BERT takes a bidirectional approach, which is better suited for other types of tasks, for example, classification. Lastly, BART combines...