Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
25 Feb. 2022
Author: Sebastian Raschka ,Yuxi (Hayden) Liu ,Vahid Mirjalili,Dmytro Dzhulgakov (Foreword)
Publisher finelybook 出版社: Packt Publishing (25 Feb. 2022)
Language 语言: English
Print Length 页数: 770 pages
ISBN-10: 1801819319
ISBN-13: 9781801819312
Book Description
By finelybook
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch’s simple to code framework
Key Features
Learn applied machine learning with a solid foundation in theory
Clear, intuitive explanations take you deep into the theory and practice of Python machine learning
Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices
Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-Author: -step tutorial and a reference you’ll keep coming back to as you build your machine learning systems.
Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.
Why PyTorch?
PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.
You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, 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).
This PyTorch book is your companion to machine learning with Python, whether you’re a Python developer new to machine learning or want to deepen your knowledge of the latest developments.
What you will learn
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
