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...
patreon: / codeemporium
REFERENCES
[1] Autoencoders: https://www.deeplearningbook.org/cont...
[2] Sparse autoencoder (last part): https://web.stanford.edu/class/cs294a...
[3] Why are sparse encoders sparse?: https://www.quora.com/Why-are-sparse-...
[4] KL Divergence: https://en.wikipedia.org/wiki/Kullbac...
[5] Semantic Hashing: https://www.cs.utoronto.ca/~rsalakhu/...
[6] Variational Autoencoders: https://jaan.io/what-is-variational-a...
[7] Xander’s video on Variational AutoEncoders (Arxiv Insights): • Variational Autoencoders
CLIPS
[1] Karol Majek’s Self driving car with RCNN: • Mask RCNN - COCO - instance segmentation
[2] Auto encoder images: https://www.jeremyjordan.me/autoencod...
[3] Semantic Segmentation with Autoencoders: https://github.com/arahusky/Tensorflo...
[4] Neural Inpainting paper: https://arxiv.org/pdf/1611.09969.pdf
[5] GAN results: • Progressive Growing of GANs for Improved Q...
#machinelearning #deeplearning #neuralnetwork #ai #datascience
На этой странице сайта вы можете посмотреть видео онлайн Autoencoders - EXPLAINED длительностью часов минут секунд в хорошем качестве, которое загрузил пользователь CodeEmporium 17 Ноябрь 2018, поделитесь ссылкой с друзьями и знакомыми, на youtube это видео уже посмотрели 81,593 раз и оно понравилось 1.6 тысяч зрителям. Приятного просмотра!
