Book description
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.
By the end of this book, you will be able to confidently build neural network models using PyTorch.
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
Who This Book Is For
Machine learning engineers, data scientists and Python programmers and software developers interested in learning the PyTorch framework.
Table of contents
- Cover
- Front Matter
- 1. Introduction to PyTorch, Tensors, and Tensor Operations
- 2. Probability Distributions Using PyTorch
- 3. CNN and RNN Using PyTorch
- 4. Introduction to Neural Networks Using PyTorch
- 5. Supervised Learning Using PyTorch
- 6. Fine-Tuning Deep Learning Models Using PyTorch
- 7. Natural Language Processing Using PyTorch
- 8. Distributed PyTorch Modelling, Model Optimization, and Deployment
- 9. Data Augmentation, Feature Engineering, and Extractions for Image and Audio
- 10. PyTorch Model Interpretability and Interface to Sklearn
- Back Matter
Product information
- Title: PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models
- Author(s):
- Release date: December 2022
- Publisher(s): Apress
- ISBN: 9781484289259
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