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Intro

Sequences in the wild

A sequence modeling problem: predict the next word

use a fixed window

can't model long-term dependencies

use entire sequence as set of counts

counts don't preserve order

use a really big fixed window

no parameter sharing

Sequence modeling: design criteria

Standard feed-forward neural network

Recurrent neural networks: sequence modeling

A standard "vanilla" neural network

A recurrent neural network (RNN)

RNN state update and output

RNNs: computational graph across time

Recall: backpropagation in feed forward models

RNNs: backpropagation through time

Standard RNN gradient flow: exploding gradients

Standard RNN gradient flow:vanishing gradients

The problem of long-term dependencies

Trick #1: activation functions

Trick #2: parameter initialization

Standard RNN In a standard RNN repeating modules contain a simple computation node

Long Short Term Memory (LSTMs)

LSTMs: forget irrelevant information

LSTMs: output filtered version of cell state

LSTM gradient flow

Example task: music generation

Example task: sentiment classification

Example task: machine translation

Attention mechanisms

Recurrent neural networks (RNNs)

Description:

Explore deep sequence modeling with recurrent neural networks in this lecture from MIT's Introduction to Deep Learning course. Dive into the challenges of modeling long-term dependencies in sequences and learn about various RNN architectures, including standard RNNs and Long Short-Term Memory (LSTM) networks. Discover techniques for addressing gradient flow issues, such as exploding and vanishing gradients. Examine practical applications of RNNs in music generation, sentiment classification, and machine translation. Gain insights into attention mechanisms and their role in improving sequence modeling performance. Enhance your understanding of deep learning techniques for processing and generating sequential data.

MIT: Recurrent Neural Networks

Alexander Amini

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#Computer Science #Artificial Intelligence #Neural Networks #Recurrent Neural Networks (RNN) #Deep Learning #Attention Mechanisms #Backpropagation #Machine Learning #Sequence Modeling