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

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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

Implementing a GNN using the PyTorch Geometric library

In this section, we will implement a GNN using the PyTorch Geometric library, which simplifies the process of training GNNs. We apply the GNN to QM9, a dataset consisting of small molecules, to predict isotropic polarizability, which is a measure of a molecule’s tendency to have its charge distorted by an electric field.

Installing PyTorch Geometric

PyTorch Geometric can be installed via conda or pip. We recommend you visit the official documentation website at https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html to select the installation command recommended for your operating system. For this chapter, we used pip to install version 2.0.2 along with its torch-scatter and torch-sparse dependencies:

pip install torch-scatter==2.0.9
pip install torch-sparse==0.6.12
pip install torch-geometric==2.0.2

Let’s start by loading a dataset of small molecules and look at...

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Table of Contents (22) Chapters