RNN

See also: Machine learning terms

Introduction

In the field of machine learning, Recurrent Neural Networks (RNNs) are a class of artificial neural networks that are designed to process sequences of data. RNNs have gained significant popularity in recent years, particularly for tasks involving natural language processing, time series analysis, and speech recognition. Unlike traditional feedforward neural networks, RNNs possess a unique architecture that allows them to maintain an internal state, making them well-suited for handling data with temporal or sequential dependencies.

Architecture

Basic Structure

Recurrent Neural Networks consist of input, hidden, and output layers, similar to other neural networks. However, the key difference lies in the hidden layer, which possesses recurrent connections. These connections enable the network to maintain an internal state by looping the hidden layer's output back to itself, effectively "remembering" information from previous time steps.

The hidden layer is typically composed of a set of neurons, each of which receives input not only from the previous layer but also from its own output at the previous time step. This recurrent connection allows the RNN to learn and model temporal dependencies within the input data.

Activation Functions

In RNNs, activation functions play a crucial role in determining the output of neurons and the overall behavior of the network. Commonly used activation functions in RNNs include the hyperbolic tangent (tanh) and rectified linear unit (ReLU) functions. These functions introduce non-linearity into the model, enabling RNNs to learn complex patterns in the input data.

Training

RNNs are trained using a variant of the backpropagation algorithm known as backpropagation through time (BPTT). BPTT unfolds the RNN through time, converting it into a deep feedforward network with multiple layers, each representing a time step. The algorithm computes the gradients of the loss function with respect to the network's weights, and these gradients are then used to update the weights via an optimization algorithm, such as stochastic gradient descent.

Challenges and Limitations

Vanishing and Exploding Gradients

Training RNNs can be challenging due to the vanishing and exploding gradient problems. During backpropagation, gradients can become very small (vanish) or very large (explode), making it difficult for the network to learn long-term dependencies. This issue arises because of the recurrent connections and the repeated application of the same weight matrices during BPTT.

Long Short-Term Memory (LSTM)

To address the vanishing gradient problem, the Long Short-Term Memory (LSTM) architecture was introduced by Hochreiter and Schmidhuber in 1997. LSTMs are a type of RNN that uses specialized memory cells, which can store information for extended periods. These memory cells are equipped with gating mechanisms that control the flow of information, enabling LSTMs to learn long-term dependencies effectively.

Explain Like I'm 5 (ELI5)

Recurrent Neural Networks (RNNs) are a type of computer brain that can remember things from the past. Imagine if you were watching a movie, and you could only see one frame at a time without remembering what happened before. It would be hard to understand the story, right? RNNs are good at understanding stories because they can remember what happened before and use that information to make sense of what's happening now. They're great for tasks like understanding language, predicting what will happen next in a sequence, or even understanding what someone is saying when they talk!