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Machine Learning with PyTorch and Scikit-Learn
Machine Learning with PyTorch and Scikit-Learn

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

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Profile Icon Sebastian Raschka Profile Icon Vahid Mirjalili Profile Icon Yuxi (Hayden) Liu
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zł39.99 zł177.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (95 Ratings)
eBook Feb 2022 774 pages 1st Edition
eBook
zł39.99 zł177.99
Paperback
zł221.99
Subscription
Free Trial
Arrow left icon
Profile Icon Sebastian Raschka Profile Icon Vahid Mirjalili Profile Icon Yuxi (Hayden) Liu
Arrow right icon
zł39.99 zł177.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (95 Ratings)
eBook Feb 2022 774 pages 1st Edition
eBook
zł39.99 zł177.99
Paperback
zł221.99
Subscription
Free Trial
eBook
zł39.99 zł177.99
Paperback
zł221.99
Subscription
Free Trial

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Machine Learning with PyTorch and Scikit-Learn

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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Feb 25, 2022
Length: 774 pages
Edition : 1st
Language : English
ISBN-13 : 9781801816380
Category :

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Product Details

Publication date : Feb 25, 2022
Length: 774 pages
Edition : 1st
Language : English
ISBN-13 : 9781801816380
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Table of Contents

21 Chapters
Giving Computers the Ability to Learn from Data Chevron down icon Chevron up icon
Training Simple Machine Learning Algorithms for Classification Chevron down icon Chevron up icon
A Tour of Machine Learning Classifiers Using Scikit-Learn Chevron down icon Chevron up icon
Building Good Training Datasets – Data Preprocessing Chevron down icon Chevron up icon
Compressing Data via Dimensionality Reduction Chevron down icon Chevron up icon
Learning Best Practices for Model Evaluation and Hyperparameter Tuning Chevron down icon Chevron up icon
Combining Different Models for Ensemble Learning Chevron down icon Chevron up icon
Applying Machine Learning to Sentiment Analysis Chevron down icon Chevron up icon
Predicting Continuous Target Variables with Regression Analysis Chevron down icon Chevron up icon
Working with Unlabeled Data – Clustering Analysis Chevron down icon Chevron up icon
Implementing a Multilayer Artificial Neural Network from Scratch Chevron down icon Chevron up icon
Parallelizing Neural Network Training with PyTorch Chevron down icon Chevron up icon
Going Deeper – The Mechanics of PyTorch Chevron down icon Chevron up icon
Classifying Images with Deep Convolutional Neural Networks Chevron down icon Chevron up icon
Modeling Sequential Data Using Recurrent Neural Networks Chevron down icon Chevron up icon
Transformers – Improving Natural Language Processing with Attention Mechanisms Chevron down icon Chevron up icon
Generative Adversarial Networks for Synthesizing New Data Chevron down icon Chevron up icon
Graph Neural Networks for Capturing Dependencies in Graph Structured Data Chevron down icon Chevron up icon
Reinforcement Learning for Decision Making in Complex Environments Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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