NYU Deep Learning SP21 - 04.2 – Recurrent neural networks, vanilla and gated (LSTM)

Course website: http://bit.ly/DLSP21-web Playlist: http://bit.ly/DLSP21-YouTube Speaker: Alfredo Canziani Chapters 00:00 – Good morning 00:22 – How to summarise papers (as @y0b1byte) with Notion 05:05 – Why do we need to go to a higher hidden dimension? 11:03 – Today class: recurrent neural nets 16:12 – Vector to sequence (vec2seq) 23:01 – Sequence to vector (seq2vec) 27:41 – Sequence to vector to sequence (seq2vec2seq) 35:27 – Sequence to sequence (seq2seq) 38:35 – Training a recurrent network: back propagation through time 47:51 – Training example: language model 51:06 – Vanishing & exploding gradients and gating mechanism 53:32 – The Long Short-Term Memory (LSTM) 57:34 – Jupyter Notebook and PyTorch in action: sequence classification 1:04:46 – Inspecting the activation values 1:05:00 – Closing remarks