Machine Learning with PyTorch and Scikit-Learn - (Paperback)

About the Book

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.

Book Synopsis

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.

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, 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

Who this book is for:

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.

Table of Contents

- Giving Computers the Ability to Learn from Data

- Training Simple Machine Learning Algorithms for Classification

- A Tour of Machine Learning Classifiers Using Scikit-Learn

- Building Good Training Datasets - Data Preprocessing

- Compressing Data via Dimensionality Reduction

- Learning Best Practices for Model Evaluation and Hyperparameter Tuning

(N.B. Please use the Read Sample option to see further chapters)

Review Quotes

"I'm confident that you will find this book invaluable both as a broad overview of the exciting field of machine learning and as a treasure of practical insights. I hope it inspires you to apply machine learning for the greater good in your problem area, whatever it might be."

--

Dmytro Dzhulgakov, PyTorch Core Maintainer

"This 700-page book covers most of today's widely used machine learning algorithms, and will be especially useful to anybody who wants to understand modern machine learning through examples of working code. It covers a variety of approaches, from basic algorithms such as logistic regression to very recent topics in deep learning such as BERT and GPT language models and generative adversarial networks. The book provides examples of nearly every algorithm it discusses in the convenient form of downloadable Jupyter notebooks that provide both code and access to datasets. Importantly, the book also provides clear instructions on how to download and start using state-of-the-art software packages that take advantage of GPU processors, including PyTorch and Google Colab."

--

Tom Mitchell, Professor CMU, Founder of CMU's Machine Learning Department