Long Short-Term Memory (LSTM) Networks: Overcoming Vanishing Gradients

Learning Objectives Explain the vanishing gradient problem and its impact on simple Recurrent Neural Networks (RNNs). Diagram the core components of an LSTM cell, including the cell state, hidden state, and gates. Describe the function of the forget gate, input gate, and output gate in managing information flow. Trace the flow of information through an LSTM cell for a given short sequence of data. Articulate how the LSTM's gate mechanism and cell state mitigate the vanishing gradient problem. Compare and contrast the architecture and capabilities of LSTMs with those of simple RNNs. Ever read a long paragraph and forget the beginning by the time you reach the end? 🤔 That's the same problem simple AI models face with long sequences of data! This tutorial explores L...

TermDefinitionExample Recurrent Neural Network (RNN)A type of neural network designed to work with sequence data (like text or time series) by having loops, allowing information to persist from one step to the next.When predicting the next word in 'the clouds are in the ___', an RNN uses the memory of the preceding words ('the clouds are in') to infer that 'sky' is a likely answer. Vanishing Gradient ProblemA major issue in training deep or recurrent networks where the gradients (signals used for learning) shrink exponentially as they are propagated back in time, making it impossible for the network to learn long-range dependencies.In the sentence 'I grew up in France... I speak fluent ___', a simple RNN might forget 'France' by the time i...

Forget Gate (f_t) f_t = σ(W_f * [h_{t-1}, x_t] + b_f) Decides what information to throw away from the cell state. It looks at the previous hidden state (h_{t-1}) and the current input (x_t) and outputs a number between 0 and 1 for each piece of information in the previous cell state (C_{t-1}). A 1 means 'completely keep this' while a 0 means 'completely get rid of this'. Input Gate (i_t) i_t = σ(W_i * [h_{t-1}, x_t] + b_i) and C̃_t = tanh(W_C * [h_{t-1}, x_t] + b_C) Decides what new information to store in the cell state. It has two parts: the sigmoid layer (i_t) decides which values to update, and the tanh layer (C̃_t) creates a vector of new candidate values that could be added to the state. Output Gate (o_t) o_t = σ(W_o * [h_{t-1}, x_t] + b_o) an...