Autoencoders are neural networks designed to learn representations of datasets (encoding) with the purpose of reconstructing them even if they contain noisy data (decoding). Autoencoders use unsupervised learning techniques to extract information from datasets and learn from them.
This repository contains a notebook built with Google Colab that shows how to use Keras to create an Autoencoder that processes the Fashion MNIST dataset. This is a dataset of 60,000 28x28 grayscale images of 10 fashion categories along with a test set of 10,000 images.
Keras is an open-source deep-learning framework written in Python. We can use it to build and evaluate deep learning models.
You can open the autoencoder_fashion_mnist.ipynb notebook using Google Colab or Jupyter Notebooks to test it.
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Deep Learning Book - Introduction to Autoencoders (content in Brazilian Portuguese)