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This book sets out to be a comprehensive guide to machine learning and deep learning with PyTorch, and to act as both a step-by-step tutorial and a reference. Sebastian Raschka and Yuxi (Hayden) Liu provide explanations, visualizations, and examples, to cover the essential machine learning techniques in depth. This new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).
<ASIN:1801819319>
The book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. It also covers generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning.
Author: Sebastian Raschka , Yuxi (Hayden) Liu
Publisher: Packt
Date: February 2022
Pages: 770
ISBN: 978-1801819312
Print: 1801819319
Kindle: B09NW48MR1
Audience: Python developers interested in machine learning
Level: Intermediate/Advanced
Category: Artificial Intelligence
- Explore frameworks, models, and techniques for machines to 'learn' from data
- Use scikit-learn for machine learning and PyTorch for deep learning
- Train machine learning classifiers on images, text, and more
- Build and train neural networks, transformers, and boosting algorithms
- Discover best practices for evaluating and tuning models
- Predict continuous target outcomes using regression analysis
- Dig deeper into textual and social media data using sentiment analysis
For recommendations of books on Machine Learning see AI Books To Inspire You in our Programmer's Bookshelf section. It includes Python Machine Learning, 3rd Ed by Sebastian Raschka and Vahid Mirjalili which was given a 5-star rating by Mike James.
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