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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

Profile Icon Sebastian Raschka Profile Icon Yuxi (Hayden) Liu Profile Icon Vahid Mirjalili
By Sebastian Raschka , Yuxi (Hayden) Liu , Vahid Mirjalili
$29.99 $43.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (8 Ratings)
Book Feb 2022 774 pages 1st Edition
eBook
$29.99 $43.99
Print
$54.99
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Free Trial
Renews at $19.99p/m
Profile Icon Sebastian Raschka Profile Icon Yuxi (Hayden) Liu Profile Icon Vahid Mirjalili
By Sebastian Raschka , Yuxi (Hayden) Liu , Vahid Mirjalili
$29.99 $43.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (8 Ratings)
Book Feb 2022 774 pages 1st Edition
eBook
$29.99 $43.99
Print
$54.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$29.99 $43.99
Print
$54.99
Subscription
Free Trial
Renews at $19.99p/m

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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.

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 Edition
Language : English
ISBN-13 : 9781801819312
Category :

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Product feature icon DRM FREE - Read whenever, wherever and however you want

Product Details

Publication date : Feb 25, 2022
Length 774 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781801819312
Category :

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Frequently bought together

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Machine Learning with PyTorch and Scikit-Learn
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Deep Learning with TensorFlow and Keras – 3rd edition
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Book stack Total $ 101.96 147.97 46.01 saved Stars icon

Table of Contents

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

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