Autoencoder In PyTorch - Theory & Implementation

In this Deep Learning Tutorial we learn how Autoencoders work and how we can implement them in PyTorch.


In this Deep Learning Tutorial we learn how Autoencoders work and how we can implement them in PyTorch.

An autoencoder is not used for supervised learning. We will no longer try to predict something about our input. Instead, an autoencoder is considered a generative model: It learns a distributed representation of our training data, and can even be used to generate new instances of the training data.

An autoencoder model contains two components:

  • An encoder that takes an image as input, and outputs a low-dimensional embedding (representation) of the image.
  • A decoder that takes the low-dimensional embedding, and reconstructs the image.

Resource: https://www.cs.toronto.edu/~lczhang/360/lec/w05/autoencoder.html


FREE VS Code / PyCharm Extensions I Use

✅ Write cleaner code with Sourcery, instant refactoring suggestions: Link*


Python Problem-Solving Bootcamp

🚀 Solve 42 programming puzzles over the course of 21 days: Link*

* These are affiliate link. By clicking on it you will not have any additional costs. Instead, you will support my project. Thank you! 🙏