Machine Learning with PyTorch and Scikit-Learn
Develop machine learning and deep learning models with Python
Published on 25. February 2022
Book
Paperback/Softback
774 pages
978-1-80181-931-2 (ISBN)
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.
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 DescriptionMachine 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 forIf 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.
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 DescriptionMachine 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 forIf 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.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 42 mm
Weight
1417 gr
ISBN-13
978-1-80181-931-2 (9781801819312)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Sebastian Raschka | Yuxi (Hayden) Liu | Vahid Mirjalili
Machine Learning with PyTorch and Scikit-Learn
Develop machine learning and deep learning models with Python
E-Book
06/2024
1st Edition
Packt Publishing Limited
from
€29.99
Available for download
Persons
Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence. Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retrieval.
He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages. Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.
He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages. Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.
Content
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
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
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