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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

Product type Paperback

Published in May 2024

Publisher Packt

ISBN-13 9781801074308

Length 558 pages

Edition 2nd Edition

Tools

PyTorch

Concepts

Deep Learning

Author (1):

Ashish Ranjan Jha

View More author details

Table of Contents (21) Chapters

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. Index

Index

A

Accelerate 277, 499, 508

using, to speed up PyTorch model training 508-510

actions 300

action-value function 304

activation functions 10, 21

leaky ReLU 12, 13

rectified linear units (ReLUs) 11, 12

sigmoid 10, 11

TanH 11

actor-critic method 303

Adadelta 14, 15

Adagrad 14

Adam optimizer 15, 16, 38, 105

agent 300

agent, training in Model-Free RL setting

policy optimization 303, 304

Q-learning 304

AlexNet 44, 55

fine-tuning 55-58

fine-tuning, with PyTorch 58-65

AlphaZero 303

Amazon Machine Image (AMI) 381

Amazon SageMaker

TorchServe, using with 383

Amazon Web Services (AWS) 347, 381, 508

PyTorch, using with 381

Android

PyTorch model, deploying on 392

Android app

development environment, setting up 393-395

phone camera, using to capture images 395, 396

Android mobile device

app, launching 405-409

Android NDK (Native Development Kit) 393

Android...

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Authors (1)

Ashish Ranjan Jha

Ashish Ranjan Jha

Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), a master's degree in Computer Science from EPFL (Switzerland), and an MBA degree from Quantic School of Business (Washington). He has received a distinction in all 3 of his degrees. He has worked for large technology companies, including Oracle and Sony as well as the more recent tech unicorns such as Revolut, mostly focused on artificial intelligence. He currently works as a machine learning engineer. Ashish has worked on a range of products and projects, from developing an app that uses sensor data to predict the mode of transport to detecting fraud in car damage insurance claims. Besides being an author, machine learning engineer, and data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around machine learning.

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