/machine-learning-book

Code Repository for Machine Learning with PyTorch and Scikit-Learn

Primary LanguageJupyter NotebookMIT LicenseMIT

Machine Learning with PyTorch and Scikit-Learn Book

Code Repository

Paperback: 770 pages
Publisher: Packt Publishing
Language: English

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

Links

Table of Contents and Code Notebooks

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}  
}