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Recurrent Neural Networks, Vanilla and Gated (LSTM)

Difficulty level

This lecture covers the concept of recurrent neural networks: vanilla and gated (LSTM) and is a part of the Deep Learning Course at NYU's Center for Data Science.

Topics covered in this lesson


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