In this video, we talk about Convolutional Neural Networks. Give the video a thumbs up and hit that SUBSCRIBE button for more awesome content.
Code to demonstrate Equivariance wrt Translation: https://github.com/ajhalthor/cnn-notes/blob/master/trans_conv_combined.py
My video on Generative Adversarial Networks: https://www.youtube.com/watch?v=O8LAi6ksC80
INVESTING
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Music at: https://www.bensound.com/royalty-free-music/track/tenderness
REFERENCES
First CNN Paper: http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf
The Deep Learning Book for details: http://www.deeplearningbook.org/contents/convnets.html
DCGAN Paper: https://arxiv.org/abs/1511.06434
About CNNs : https://cambridgespark.com/content/tutorials/convolutional-neural-networks-with-keras/index.html
Flatten Vs. FC1: https://alexisbcook.github.io/2017/global-average-pooling-layers-for-object-localization/
Diffference between CNN & MLP: https://www.quora.com/What-is-the-difference-between-a-convolutional-neural-network-and-a-multilayer-perceptron
Nine Deep learning papers you should know about: https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html
Global Average Pooling (GAP) Layers: https://alexisbcook.github.io/2017/global-average-pooling-layers-for-object-localization/
Equivariance: https://arxiv.org/pdf/1411.5908.pdf
CNN slides based on the deep learning book (birds eye read): http://www.cedar.buffalo.edu/~srihari/CSE676/9.2%20CNN-Motivation.pdf
Google's Deep Mind Learns how to walk: https://www.youtube.com/watch?v=gn4nRCC9TwQ
Generate CNNs visually : https://github.com/yu4u/convnet-drawer
Playing Atari with Deep Reinforecemnt Learning: https://arxiv.org/pdf/1312.5602v1.pdf
Deep Mind's Q-Learning playing Atari games: https://www.youtube.com/watch?v=V1eYniJ0Rnk