Parameter Sharing - Recurrent and Convolutional Nets

This course explains parameter sharing in recurrent and convolutional neural networks. It covers RNNs, GRUs, LSTMs, attention for sequence-to-sequence mapping, convolutional operations, backpropagation, and CNN architectures.

Syllabus

– Welcome to class
– Hypernetworks
– Shared weights
– Parameter sharing ⇒ adding the gradients
– Max and sum reductions
– Recurrent nets
– Unrolling in time
– Vanishing and exploding gradients
– Math on the whiteboard
– RNN tricks
– RNN for differential equations
– GRU
– What is a memory
– LSTM – Long Short-Term Memory net
– Multilayer LSTM
– Attention for sequence to sequence mapping
– Convolutional nets
– Detecting motifs in images
– Convolution definitions
– Backprop through convolutions
– Stride and skip: subsampling and convolution “à trous”
– Convolutional net architecture
– Multiple convolutions
– Vintage ConvNets
– How does the brain interpret images?
– Hubel & Wiesel's model of the visual cortex
– Invariance and equivariance of ConvNets
– In the next episode…
– Training time, iteration cycle, and historical remarks