Machine Learning with PyTorch and Scikit-Learn

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, 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.

Type
ebook
Category
Data
publication date
2022-02-25
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

no of pages
774
duration
1548
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
approach
The authors’ unique insight and expertise introduce machine learning and deep learning algorithms, and show you how to apply them to practical industry challenges using real-world examples.
audience
If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.

Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.
meta description
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.

Purchase of the print or Kindle book includes a free eBook in PDF format.
short description
Fully updated with PyTorch and the latest additions to scikit-learn. 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.
subtitle
Develop machine learning and deep learning models with Python
keywords
data analysis; data science; scikit learn pytorch; pytorch deep learning; pytorch nlp; sklearn; fastai
Product ISBN
9781801819312