Machine Learning with PyTorch and Scikit-Learn

Develop machine learning and deep learning models with Python

Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili, Dmytro Dzhulgakov

• Publisher: Packt Publishing
• Year: 2022
• eBook ISBN: 9781801816380
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

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.

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Index

Symbols

5×2 cross-validation 192

7-Zip

URL 248

A

accuracy

versus classification error 57

action-value function 682

estimation, with Monte Carlo 688

greedy policy, computing from 689

activation function, for multilayer neural network

selecting 400

activation functions, torch.nn module

reference link 406

activations

computing, in RNNs 504, 505

AdaBoost

applying, with scikit-learn 233-236

comparing, with gradient boosting 237

AdaBoost recognition 229

Adam optimizer 479

adaptive boosting

weak learners, leveraging 229

working 229-233

Adaptive Linear Neuron (Adaline) 35-37, 278

algorithm 337

implementation, converting into algorithm for logistic regression 66-68

implementing, in Python 39-43

advanced graph neural network literature

pointers 669, 670

agent 6, 674, 675

agglomerative clustering

applying, via scikit-learn 327, 328

AI winters

reference link 336

Ames Housing dataset 272

loading, into data frame 272-274

nonlinear relationships, modeling 297-299

Anaconda 15

reference link 15

Anaconda Python distribution

using 15, 16

artificial intelligence (AI) 1, 19, 336, 452

artificial neural networks

training 360

used, for modeling complex functions 335, 336

artificial neuron 20

attention mechanism

for RNNs 540, 542

attention weights

computing 544

autoencoders 590, 591

automatic differentiation 363, 416

autoregression 53...

Table of contents

• Preface
• 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
• Other Books You May Enjoy
• Index