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.

In addition, Zbynek Bazanowski contributed this helpful guide explaining how to run the code examples on Google Colab.

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}  
}

​

Please see the ch01/README.md file for setup recommendations.

• Serbian Translation: Mašinsko učenje uz PyTorch i Scikit-Learn. ISBN: 9788673105772