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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

Recommendation Systems with PyTorch

Recommendation systems are everywhere, like Netflix and YouTube, which recommend what to watch; Spotify, which recommends what to listen to; LinkedIn, which suggests jobs; and Amazon, which recommends which products to buy.

Figure 18.1: Examples of different recommendation systems. Starting from top to bottom, followed by left to right – Netflix, Spotify, LinkedIn, YouTube, and Amazon

A recommendation system is an algorithm that provides personalized suggestions to users. The main goal is to predict what product(s) a user may be interested in, based on their preferences, behaviors, similarity with existing users, and interactions with the system. Most of today’s recommendation systems are powered by an underlying deep learning model. Such models predict if a user will like a product (a movie, a book, a podcast, a person on the web, etc.) based on the existing consumption patterns of this and the other users in the system...

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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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