PyTorch book of the bestselling and widely acclaimed Python Machine Learning series expanded to include transformers, XGBoost, and graph neural networks

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

Book Description:

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-by-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, this 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 you the principles to build models and applications for yourself.

Updated to cover deep learning using PyTorch, this book also introduces readers to the latest additions to scikit-learn. Moreover, this book covers various machine learning and deep learning techniques for text and image classification. 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 also expanded to cover the latest trends in deep learning, including introductions to 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 graph neural networks
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

Who this book is for:

If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource.

Written for developers and data scientists who want to create practical machine learning with Python and PyTorch deep learning code. This Python book is ideal for anyone who wants to teach computers how to learn from data.

Working knowledge of the Python programming language, along with a good understanding of calculus and linear algebra is a must.

Item Information Item Information

Format: TradePaperback
Author: Raschka, Sebastian
ISBN: 9781801819312
Condition: Used
Dimensions: 9.25 x 1.53
Number Of Pages: 770
Publication Year: 2022

Language: English

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

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