Pradeepta Mishra
PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models
PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models
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- More about PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models
This book provides code snippets and examples to help you build neural network models using PyTorch, including chapters on distributed modeling, deploying in production, and recent developments. It covers topics such as tensors, probability distribution, convolutional neural networks, and more, with a focus on scaling and best practices.
Format: Paperback / softback
Length: 266 pages
Publication date: 08 December 2022
Publisher: APress
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.
Table of Contents
Chapter 1: Introduction to PyTorch
Chapter 2: Tensor Operations in PyTorch
Chapter 3: Neural Network Models with PyTorch
Chapter 4: Probability Distribution Concepts in PyTorch
Chapter 5: Supervised and Unsupervised Learning with PyTorch
Chapter 6: Building Models with Convolutional Neural Networks
Chapter 7: Building Models with Deep Neural Networks
Chapter 8: Building Models with Recurrent Neural Networks
Chapter 9: Scorch: A Compatible Module Equivalent to Scikit-Learn
Chapter 10: Model Quantization to Reduce Parameter Size
Chapter 11: Preparing a Model for Deployment within a Production System
Chapter 12: Distributed Parallel Processing for Balancing PyTorch Workloads
Chapter 13: Using PyTorch for Image Processing
Chapter 14: Using PyTorch for Audio Analysis
Chapter 15: Model Interpretation with PyTorch
Chapter 16: Conclusion
Weight: 559g
Dimension: 254 x 178 (mm)
ISBN-13: 9781484289242
Edition number: 2nd ed.
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