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Book Overview & Buying
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Table Of Contents
Machine Learning with PyTorch and Scikit-Learn
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Machine Learning with PyTorch and Scikit-Learn
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Overview of this book
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.
Table of Contents (22 chapters)
Preface
Free Chapter
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
Combining Different Models for Ensemble Learning
Applying Machine Learning to Sentiment Analysis
Predicting Continuous Target Variables with Regression Analysis
Working with Unlabeled Data – Clustering Analysis
Implementing a Multilayer Artificial Neural Network from Scratch
Parallelizing Neural Network Training with PyTorch
Going Deeper – The Mechanics of PyTorch
Classifying Images with Deep Convolutional Neural Networks
Modeling Sequential Data Using Recurrent Neural Networks
Transformers – Improving Natural Language Processing with Attention Mechanisms
Generative Adversarial Networks for Synthesizing New Data
Graph Neural Networks for Capturing Dependencies in Graph Structured Data
Reinforcement Learning for Decision Making in Complex Environments
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Index
, is referred to as the sigmoid function for brevity, which is common in machine learning literature. In the following subsections, you will learn more about alternative nonlinear functions that are useful for implementing multilayer NNs.
