Autoencoders - EXPLAINED
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.
For more content, hit that SUBSCRIBE button, ring that bell.
Subscribe now for more awesome content: http://www.youtube.com/c/CodeEmporium?sub_confirmation=1
patreon: https://www.patreon.com/CodeEmporium
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
Видео Autoencoders - EXPLAINED канала CodeEmporium
- 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.
For more content, hit that SUBSCRIBE button, ring that bell.
Subscribe now for more awesome content: http://www.youtube.com/c/CodeEmporium?sub_confirmation=1
patreon: https://www.patreon.com/CodeEmporium
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
Видео Autoencoders - EXPLAINED канала CodeEmporium
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Variational AutoencodersEncoder Decoder Network - ComputerphileMask Region based Convolution Neural Networks - EXPLAINED!Ali Ghodsi, Lec : Deep Learning, Variational Autoencoder, Oct 12 2017 [Lect 6.2]Lecture 11 | Detection and SegmentationTransformer Neural Networks - EXPLAINED! (Attention is all you need)Boosting - EXPLAINED!Unsupervised Learning with Autoencoders | Christoph HenkelmannSimple Explanation of AutoEncodersVariational Autoencoders - EXPLAINED!Autoencoders Made Easy! (with Convolutional Autoencoder)BERT Neural Network - EXPLAINED!A Friendly Introduction to Generative Adversarial Networks (GANs)A friendly introduction to Convolutional Neural Networks and Image RecognitionBatch Normalization - EXPLAINED!Lecture 15.1 — From PCA to autoencoders — [ Deep Learning | Geoffrey Hinton | UofT ]179 - Variational autoencoders using keras on MNIST dataSequence To Sequence Learning With Neural Networks| Encoder And Decoder In-depth IntuitionAutoencoders Explained Easily178 - An introduction to variational autoencoders (VAE)