Autoencoder Explained

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How does an autoencoder work? Autoencoders are a type of neural network that reconstructs the input data its given. But we don’t care about the output, we care about the hidden representation its learned. Its a lower dimensional compression of the input that preserves its features. We can use this learned representation for tasks like image colorization, dialogue generation, and anomaly detection.

Code for this video (with Coding Challenge):
https://github.com/llSourcell/autoencoder_explained

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More learning resources:
http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders/
http://ai.stanford.edu/~quocle/tutorial2.pdf
https://lazyprogrammer.me/a-tutorial-on-autoencoders/
https://blog.keras.io/building-autoencoders-in-keras.html
https://jaan.io/what-is-variational-autoencoder-vae-tutorial/
https://hackernoon.com/autoencoders-deep-learning-bits-1-11731e200694

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13 Comments

  1. Hello Siraj, are vanilla autoenocoders same as simple autoencoders ,,,,,I don't see much research papers on this ,,, can this autoencoders be implemented using spectral data ,,,,as most of the examples are on MNIST dataset,,,, i need to visualize , pca vs t-SNE vs autoencoders

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