PyTorch recipes : A Problem-Solution Approach to Build, Train and Deploy Neural Network Models / Pradeepta Mishra.
Mishra, Pradeepta, author.
2022
QA76.73.P98
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Title PyTorch recipes : A Problem-Solution Approach to Build, Train and Deploy Neural Network Models / Pradeepta Mishra.
Author Mishra, Pradeepta, author.
Edition Second edition.
ISBN 9781484289259 (electronic bk.)
1484289250 (electronic bk.)
9781484289242
1484289242
DOI https://doi.org/10.1007/978-1-4842-8925-9
Published Berkeley, CA : Apress L. P., 2022.
Language English
Description 1 online resource (xxiv, 266 pages) : illustrations
Item Number 10.1007/978-1-4842-8925-9 doi
Call Number QA76.73.P98
Dewey Decimal Classification 006.3/2
Summary 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. 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.
Note Description based upon print version of record.
Access Note Access limited to authorized users.
Available in Other Form PyTorch Recipes
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Intro
Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Introduction to PyTorch, Tensors, and Tensor Operations
What Is PyTorch?
PyTorch Installation
Recipe 1-1. Using Tensors
Problem
Solution
How It Works
Conclusion
Chapter 2: Probability Distributions Using PyTorch
Recipe 2-1. Sampling Tensors
Problem
Solution
How It Works
Recipe 2-2. Variable Tensors
Problem
Solution
How It Works
Recipe 2-3. Basic Statistics
Problem
Solution
How It Works

Recipe 2-4. Gradient Computation
Problem
Solution
How It Works
Recipe 2-5. Tensor Operations
Problem
Solution
How It Works
Recipe 2-6. Tensor Operations
Problem
Solution
How It Works
Recipe 2-7. Distributions
Problem
Solution
How It Works
Conclusion
Chapter 3: CNN and RNN Using PyTorch
Recipe 3-1. Setting Up a Loss Function
Problem
Solution
How It Works
Recipe 3-2. Estimating the Derivative of the Loss Function
Problem
Solution
How It Works
Recipe 3-3. Fine-Tuning a Model
Problem
Solution
How It Works

Recipe 3-4. Selecting an Optimization Function
Problem
Solution
How It Works
Recipe 3-5. Further Optimizing the Function
Problem
Solution
How It Works
Recipe 3-6. Implementing a Convolutional Neural Network (CNN)
Problem
Solution
How It Works
Recipe 3-7. Reloading a Model
Problem
Solution
How It Works
Recipe 3-8. Implementing a Recurrent Neural Network
Problem
Solution
How It Works
Recipe 3-9. Implementing a RNN for Regression Problems
Problem
Solution
How It Works
Recipe 3-10. Using PyTorch's Built-In Functions
Problem

Solution
How It Works
Recipe 3-11. Working with Autoencoders
Problem
Solution
How It Works
Recipe 3-12. Fine-Tuning Results Using Autoencoder
Problem
Solution
How It Works
Recipe 3-13. Restricting Model Overfitting
Problem
Solution
How It Works
Recipe 3-14. Visualizing the Model Overfit
Problem
Solution
How It Works
Recipe 3-15. Initializing Weights in the Dropout Rate
Problem
Solution
How It Works
Recipe 3-16. Adding Math Operations
Problem
Solution
How It Works
Recipe 3-17. Embedding Layers in RNN
Problem

Solution
How It Works
Conclusion
Chapter 4: Introduction to Neural Networks Using PyTorch
Recipe 4-1. Working with Activation Functions
Problem
Solution
How It Works
Linear Function
Bilinear Function
Sigmoid Function
Hyperbolic Tangent Function
Log Sigmoid Transfer Function
ReLU Function
Leaky ReLU
Recipe 4-2. Visualizing the Shape of Activation Functions
Problem
Solution
How It Works
Recipe 4-3. Basic Neural Network Model
Problem
Solution
How It Works
Recipe 4-4. Tensor Differentiation
Problem
Solution
How It Works
Conclusion

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