Machine Learning with PyTorch and Scikit-Learn

PyTorch Edition of the bestselling and widely acclaimed Python Machine Learning book. Revised and expanded for PyTorch, Transformers, Graph Neural Networks.

Key Features

• 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, 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.

Updated to cover deep learning using PyTorch, this edition introduces readers to its new PyTorch features, as well as 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 Network (GAN) 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

• 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, 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.

Table of Contents

1. Giving Computers the Ability to Learn from Data
2. Training Simple Machine Learning Algorithms for Classification
3. A Tour of Machine Learning Classifiers Using Scikit-Learn
4. Building Good Training Datasets - Data Preprocessing
5. Compressing Data via Dimensionality Reduction
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
7. Combining Different Models for Ensemble Learning
8. Applying Machine Learning to Sentiment Analysis
9. Predicting Continuous Target Variables with Regression Analysis
10. Working with Unlabeled Data - Clustering Analysis
11. Implementing a Multilayer Artificial Neural Network from Scratch
12. (N.B. Additional chapters to be confirmed upon publication