Deep Learning: A Beginner’s Guide to Reccurent Neural Network (RNN) Architecture
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.
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 take into account the past information in the sequence. They have the capability to retain and utilize memory of past patterns to predict future occurrences.
For example, let’s say we have a sequence of stock prices over time. A regular deep learning model would only consider the current price to predict the future price. However, an RNN can incorporate the historical price data and fluctuations to make more accurate predictions.
Moreover, sequence models can predict multiple future values in a given sequence, if necessary. Additionally, there are bidirectional models that can even predict past values based on the information that comes after them in the sequence.
What does the architecture of an RNN look like?
Let’s explore a simplified recurrent neural network (RNN) structure. We’ll consider a time sequence consisting of four time steps: T1, T2, T3, and T4. These time steps can be non-equally spaced and can be conceptually separated, such as words in a sentence.
At each time step, there is a corresponding input: X1, X2, X3, and X4. These inputs are fed into the RNN. Although the RNN remains the same throughout all time…