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Mastering PyTorch

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4.7 (20)
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Mastering PyTorch

Mastering PyTorch

4.7 (20)
By: Ashish Ranjan Jha

Overview of this book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (22 chapters)
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20
Other Books You May Enjoy
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Index

Building a recommendation system using the trained model

In this section, we create a recommendation system in the form of a function that returns a list of movie titles as recommendations for a given user. As shown in Figure 18.7, we first fetch all movies not seen by the user. Each of these movies is fed along with the given user as input to the EmbeddingNet, producing the respective predicted ratings. The movies are then sorted in decreasing order of rating, and the top-k movies are returned as recommendations.

Figure 18.7: Schematic representation of a movie recommendation system that uses an EmbeddingNet under the hood to generate ratings on movies not seen by a user

The following function performs the steps outlined in Figure 18.7:

def recommender_system(user_id, model, n_movies):
    seen_movies = set(X[X['user_id'] == user_id]['movie_id'])
    user_ratings = y[X['user_id'] == user_id]
    top_rated_movie_ids = X.loc[(X['user_id...
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Mastering PyTorch
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