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Intro

What is a sequence?

a sequence modeling problem

idea: use a fixed window

problem: we can't model long-term dependencies

idea: use entire sequence, as a set of counts

idea: use a really big fixed window

problem: no parameter sharing

to model sequences, we need

example network

RNNS remember their previous state

"unfolding" the RNN across time

remember: backpropagation

let's try it out for W with the chain rule

backpropagation through time

problem: vanishing gradient

activation functions

initialization

gated cells

possible task: music generation

possible task: machine translation

problem: a single encoding is limiting

solution: attend over all encoder states

Description:

Explore sequence modeling with neural networks in this lecture from MIT's Introduction to Deep Learning course. Delve into the challenges of modeling sequential data, understand the limitations of fixed window approaches, and discover how Recurrent Neural Networks (RNNs) address these issues. Learn about backpropagation through time, the vanishing gradient problem, and solutions like gated cells. Examine practical applications such as music generation and machine translation, and understand advanced concepts like attention mechanisms. Gain insights into activation functions, initialization techniques, and the importance of parameter sharing in sequence modeling.

Sequence Modeling with Neural Networks

Alexander Amini

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#Computer Science #Artificial Intelligence #Neural Networks #Deep Learning #Machine Learning #Activation Functions #Sequence Modeling #Art & Design #Music #Music Generation

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