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PyTorch recipes : A Problem-Solution Approach to Build, Train and Deploy Neural Network Models /

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 st...

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Detalles Bibliográficos
Clasificación:	Libro Electrónico
Autor principal:	Mishra, Pradeepta (Autor)
Formato:	Electrónico eBook
Idioma:	Inglés
Publicado:	Berkeley, CA : Apress L. P., 2022.
Edición:	Second edition.
Temas:	Neural networks (Computer science) Machine learning. Python (Computer program language) Réseaux neuronaux (Informatique) Apprentissage automatique. Python (Langage de programmation) Machine learning
Acceso en línea:	Texto completo (Requiere registro previo con correo institucional)

Tabla de Contenidos:

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

PyTorch recipes : A Problem-Solution Approach to Build, Train and Deploy Neural Network Models /

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