Autoencoders - EXPLAINED
Channel: CodeEmporium
Date: 2018-11-17

Data around us, like images and documents, are very high dimensional. Autoencoders can learn a simpler representation of it. This representation can be used in many ways:
- fast data transfers across a network
- Self driving cars (Semantic Segmentation)
- Neural Inpainting: Completing sections of an image, or removing watermarks
- Latent Semantic Hashing: Clustering similar documents together.
And the list of applications goes on.

Clearly, Autoencoders can be useful. In this video, we are going to understand it's types and functions.

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REFERENCES

[1] Autoencoders: https://www.deeplearningbook.org/contents/autoencoders.html
[2] Sparse autoencoder (last part): https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf
[3] Why are sparse encoders sparse?: https://www.quora.com/Why-are-sparse-autoencoders-sparse
[4] KL Divergence: https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence
[5] Semantic Hashing: https://www.cs.utoronto.ca/~rsalakhu/papers/semantic_final.pdf
[6] Variational Autoencoders: https://jaan.io/what-is-variational-autoencoder-vae-tutorial/
[7] Xander’s video on Variational AutoEncoders (Arxiv Insights): https://www.youtube.com/watch?v=9zKuYvjFFS8&t=1s&ab_channel=ArxivInsights

CLIPS
[1] Karol Majek’s Self driving car with RCNN: https://www.youtube.com/watch?v=OOT3UIXZztE&ab_channel=KarolMajek
[2] Auto encoder images: https://www.jeremyjordan.me/autoencoders/
[3] Semantic Segmentation with Autoencoders: https://github.com/arahusky/Tensorflow-Segmentation
[4] Neural Inpainting paper: https://arxiv.org/pdf/1611.09969.pdf
[5] GAN results: https://www.youtube.com/watch?time_continue=49&v=XOxxPcy5Gr4

#machinelearning #deeplearning #neuralnetwork #ai #datascience