Paperback: 770 pages
Publisher: Packt Publishing
Language: English

ISBN-10: 1801819319
ISBN-13: 978-1801819312
Kindle ASIN: B09NW48MR1

• Amazon link
• Packt link
• Blog post summarizing the contents

Helpful installation and setup instructions can be found in the README.md file of Chapter 1

Please note that these are just the code examples accompanying the book, which we uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.

1. Machine Learning - Giving Computers the Ability to Learn from Data [open dir]
2. Training Machine Learning Algorithms for Classification [open dir]
3. A Tour of Machine Learning Classifiers Using Scikit-Learn [open dir]
4. Building Good Training Sets – Data Pre-Processing [open dir]
5. Compressing Data via Dimensionality Reduction [open dir]
6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [open dir]
7. Combining Different Models for Ensemble Learning [open dir]
8. Applying Machine Learning to Sentiment Analysis [open dir]
9. Predicting Continuous Target Variables with Regression Analysis [open dir]
10. Working with Unlabeled Data – Clustering Analysis [open dir]
11. Implementing a Multi-layer Artificial Neural Network from Scratch [open dir]
12. Parallelizing Neural Network Training with PyTorch [open dir]
13. Going Deeper -- The Mechanics of PyTorch [open dir]
14. Classifying Images with Deep Convolutional Neural Networks [open dir]
15. Modeling Sequential Data Using Recurrent Neural Networks [open dir]
16. Transformers -- Improving Natural Language Processing with Attention Mechanisms [open dir]
17. Generative Adversarial Networks for Synthesizing New Data [open dir]
18. Graph Neural Networks for Capturing Dependencies in Graph Structured Data [open dir]
19. Reinforcement Learning for Decision Making in Complex Environments [open dir]

Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili. Machine Learning with PyTorch and Scikit-Learn. Packt Publishing, 2022.

@book{mlbook2022,  
address = {Birmingham, UK},  
author = {Sebastian Raschka, and Yuxi (Hayden) Liu, and Vahid Mirjalili},  
isbn = {978-1801819312},   
publisher = {Packt Publishing},  
title = {{Machine Learning with PyTorch and Scikit-Learn}},  
year = {2022}  
}