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: [ Ссылка ]
[2] Sparse autoencoder (last part): [ Ссылка ]
[3] Why are sparse encoders sparse?: [ Ссылка ]
[4] KL Divergence: [ Ссылка ]
[5] Semantic Hashing: [ Ссылка ]
[6] Variational Autoencoders: [ Ссылка ]
[7] Xander’s video on Variational AutoEncoders (Arxiv Insights): [ Ссылка ]

CLIPS
[1] Karol Majek’s Self driving car with RCNN: [ Ссылка ]
[2] Auto encoder images: [ Ссылка ]
[3] Semantic Segmentation with Autoencoders: [ Ссылка ]
[4] Neural Inpainting paper: [ Ссылка ]
[5] GAN results: [ Ссылка ]

#machinelearning #deeplearning #neuralnetwork #ai #datascience

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