Mishra P. - PyTorch Recipes [2023, PDF/EPUB, ENG] torrent

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PyTorch Recipes

Год издания: 2023
Автор: Mishra P.

Издательство: Apress
ISBN: 978-1-4842-8925-9
Язык: Английский

Формат: PDF/ePub
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 282

Описание: Learn how to use PyTorch to build neural network models using code snippets updated for this second edition. This book includes new chapters covering topics such as distributed PyTorch modeling, deploying PyTorch models in production, and developments around PyTorch with updated code.
You'll start by learning how to use tensors to develop and fine-tune neural network models and implement deep learning models such as LSTMs, and RNNs. Next, you'll explore probability distribution concepts using PyTorch, as well as supervised and unsupervised algorithms with PyTorch. This is followed by a deep dive on building models with convolutional neural networks, deep neural networks, and recurrent neural networks using PyTorch.
This new edition covers also topics such as Scorch, a compatible module equivalent to the Scikit machine learning library, model quantization to reduce parameter size, and preparing a model for deployment within a production system. Distributed parallel processing for balancing PyTorch workloads, using PyTorch for image processing, audio analysis, and model interpretation are also covered in detail. Each chapter includes recipe code snippets to perform specific activities.
By the end of this book, you will be able to confidently build neural network models using PyTorch.
PyTorch is a recent entrant to the league of graph computation tools/programming languages. Addressing the limitations of previous frameworks, PyTorch promises a better user experience in the deployment of deep learning models and the creation of advanced models using a combination of convolutional neural networks, recurrent neural networks, LSTMs, and deep neural networks.
What You Will Learn
- Utilize new code snippets and models to train machine learning models using PyTorch
- Train deep learning models with fewer and smarter implementations
- Explore the PyTorch framework for model explainability and to bring transparency to model interpretation
- Build, train, and deploy neural network models designed to scale with PyTorch
- Understand best practices for evaluating and fine-tuning models using PyTorch
- Use advanced torch features in training deep neural networks
- Explore various neural network models using PyTorch
- Discover functions compatible with sci-kit learn compatible models
- Perform distributed PyTorch training and execution
[spoiler="Примеры страниц"]

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[spoiler="Оглавление"]
About the Author xvii
About the Technical Reviewer xix
Acknowledgments xxi
Introduction xxiii
Chapter 1: Introduction to PyTorch, Tensors, and Tensor Operations 1
Chapter 2: Probability Distributions Using PyTorch 29
Chapter 3: CNN and RNN Using PyTorch 49
Chapter 4: Introduction to Neural Networks Using PyTorch 117
Chapter 5: Supervised Learning Using PyTorch 135
Chapter 6: Fine-Tuning Deep Learning Models Using PyTorch 157
Chapter 7: Natural Language Processing Using PyTorch 171
Chapter 8: Distributed PyTorch Modelling, Model Optimization, and Deployment 187
Chapter 9: Data Augmentation, Feature Engineering, and Extractions for Image and Audio 213
Chapter 10: PyTorch Model Interpretability and Interface to Sklearn 237
Index 261
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