Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

by Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili (Foreword by)

Overview

Packed with clear explanations, visualizations, and working examples, the book covers essential machine learning techniques in depth, along with two cutting-edge machine learning techniques: transformers and graph neural networks.

Product Details

ISBN-13:	9781837021956
Publisher:	Packt Publishing
Publication date:	02/25/2022
Pages:	774
Product dimensions:	7.50(w) x 9.25(h) x 1.63(d)

About the Author

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.

Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retrieval. He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages.

Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.

Table of Contents

Table of Contents

1. Giving Computers the Ability to Learn from Data
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