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In this deep learning project, you will learn how to build a Generative Model using Autoencoders in PyTorch
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Overview
Many of us are aware of the Discriminative models, which generally focus on predicting the data labels. In contrast, Generative Models help to generate the output, which is very similar to the input data. For example, we have some data related to cars that can be used to classify the cars, but Generative models can learn the patterns in the data and generate car features completely different than the input data.
In this project, we start by introducing Generative Models. The PyTorch framework is used to build Autoencoders on the MNIST dataset. Finally, we learn how to use Autoencoders as Generative Models followed by generating new images of digits by using the Generative Model.
Aim
To build Generative Models by using Autoencoders in PyTorch
Tech Stack
Language: Python
Libraries: torch, torchvision, torchinfo, numpy, matplotlib
Approach
Introduction to Generative Models
Introduction to Autoencoders
Buiding Autoencoders on PyTorch
Model training on Google Colab
Building Generative model by using Autoencoders
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