Understanding Recurrent Neural Networks (RNNs) – part 2 – Day 56

Understanding Recurrent Neural Networks (RNNs) Recurrent Neural Networks (RNNs) are a class of neural networks that excel in handling sequential data, such as time series, text, and speech. Unlike traditional feedforward networks, RNNs have the ability to retain information from previous inputs and use it to influence the current output, making them extremely powerful for tasks where the order of the input data matters. In day 55 article we have introduced  RNN. In this article, we will explore the inner workings of RNNs, break down their key components, and understand how they process sequences of data through time. We’ll also dive into how they are trained using Backpropagation Through Time (BPTT) and explore different types of sequence processing architectures like Sequence-to-Sequence and Encoder-Decoder Networks. What is a Recurrent Neural Network (RNN)? At its core, an RNN is a type of neural network that introduces the concept of “memory” into the model. Each neuron in an RNN has a feedback loop that allows it to use both the current input and the previous output to make decisions. This creates a temporal dependency, enabling the network to learn from past information. Recurrent Neuron: The Foundation of RNNs A recurrent neuron processes sequences…