Autoencoder Explained
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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A wonderful high-level explanation of AEs. I'm recently starting research in this field and this has been really helpful. Thanks!
Siraj has learnt a lot about teaching and is now capable of expressing complex ideas in a more didactic way. Kudoz for him.
No on the hair. Cannot watch.
6:43 had to jerk the earbuds out from that sound. Thanks for this content. It was a good introduction, I'll do more research on this
T H A N K S !
everyone got some ** for Machine learning today
I feel like im watching this on a boat on the high seas
Make a video on boltzmann machine and it's application
This covers way more than AEs. This is a very brilliant high level generalized view of what Neural Networks do and what they actually represent,
You hit a bit more than AEs and that may confuse some folks, but after a while they will realize that it was for the better as GANs and such follow these techniques.
This guy is fraud
https://www.reddit.com/r/MachineLearning/comments/d7ad2y/d_siraj_raval_potentially_exploiting_students/
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
"refund"