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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

Understanding Hugging Face within the PyTorch context

Hugging Face [1] is a rapidly growing multi-faceted AI company. On the one hand, it provides a host of libraries related to training, evaluating, optimizing, and deploying AI models. On the other hand, it is a hub of various AI models, datasets, and live AI demos (referred to as spaces in Hugging Face jargon). Hugging Face is quickly evolving into an AI community where developers are sharing cutting-edge AI work and having discussions that push the frontier of AI.

Exploring Hugging Face components relevant to PyTorch

We can view Hugging Face as a platform that contains various components, as shown in Figure 19.1. You can access the page shown in the figure on the Hugging Face website [2]. The first thing to note in this figure is the mention of PyTorch in various Hugging Face components. Hugging Face’s libraries, models, and datasets are fully compatible with PyTorch, so it is wise to discuss Hugging Face in detail...

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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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