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Mastering PyTorch

You're reading from   Mastering PyTorch Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781801074308
Length 554 pages
Edition 2nd Edition
Tools
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Author (1):
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Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
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Toc

Table of Contents (22) Chapters Close

Preface 1. Overview of Deep Learning Using PyTorch 2. Deep CNN Architectures FREE CHAPTER 3. Combining CNNs and LSTMs 4. Deep Recurrent Model Architectures 5. Advanced Hybrid Models 6. Graph Neural Networks 7. Music and Text Generation with PyTorch 8. Neural Style Transfer 9. Deep Convolutional GANs 10. Image Generation Using Diffusion 11. Deep Reinforcement Learning 12. Model Training Optimizations 13. Operationalizing PyTorch Models into Production 14. PyTorch on Mobile Devices 15. Rapid Prototyping with PyTorch 16. PyTorch and AutoML 17. PyTorch and Explainable AI 18. Recommendation Systems with PyTorch 19. PyTorch and Hugging Face 20. Other Books You May Enjoy
21. Index

Index

A

Accelerate 275, 495, 504

using, to speed up PyTorch model training 504-506

actions 298

action-value function 302

activation functions 9, 21

leaky ReLU 11, 12

rectified linear units (ReLUs) 10, 11

sigmoid 9, 10

TanH 10

actor-critic method 301

Adadelta 13, 14

Adagrad 13

Adam optimizer 14, 15, 36, 103

agent 298

agent, training in Model-Free RL setting

policy optimization 301, 302

Q-learning 302

AlexNet 42, 53

fine-tuning 53-56

fine-tuning, with PyTorch 56-63

AlphaZero 301

Amazon Machine Image (AMI) 380

Amazon SageMaker

TorchServe, using with 381, 382

Amazon Web Services (AWS) 345, 379, 504

PyTorch, using with 379

Android

PyTorch model, deploying on 390

Android app

development environment, setting up 391-393

phone camera, using to capture images 393, 394

Android mobile device

app, launching on 403-407

Android NDK (Native Development Kit) 391

...
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