Recurrent Neural Network (RNN)

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Recurrent Neural Networks (RNNs) are powerful sequence models designed to capture patterns in sequential data. This article provides an introduction to RNN architecture, training processes, and the challenge of vanishing gradients.

INTRODUCTION

RNNs, also known as sequence models, are specifically designed to model patterns in sequential data. Unlike other deep learning models that don’t consider time as a factor, RNNs excel at capturing temporal dependencies and relationships across time.

While standard deep learning models process inputs at a single point in time and generate outputs based solely on those inputs, RNNs consider the past information in the sequence. They can retain and utilize memory of past patterns to predict future occurrences.

Sequence models can predict multiple future values in a sequence. There are bidirectional models that can even predict past values based on the information that comes after them in the sequence.

ARCHITECTURE

Let’s explore a simplified recurrent neural network (RNN) structure. We’ll consider a time sequence comprising of four time steps: T1, T2, T3, and T4. These time steps can be non-equally spaced and can be conceptually…