RNN Deep Learning – Part 1 – Day 55

Understanding Recurrent Neural Networks (RNNs) and CNNs for Sequence Processing Introduction In the world of deep learning, neural networks have become indispensable, especially for handling tasks involving sequential data, such as time series, speech, and text. Among the most popular architectures for such data are Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). Although RNNs are traditionally associated with sequence processing, CNNs have also been adapted to perform well in this area. This blog will take a detailed look at how these networks work, their differences, their challenges, and their real-world applications.  Unrolling RNNs: How RNNs Process Sequences One of the most important concepts in understanding RNNs is unrolling. Unlike feedforward neural networks, which process inputs independently, RNNs have a “memory” that allows them to keep track of previous inputs by maintaining hidden states. Unrolling in Time At each time step \( t \), an RNN processes both: The current input \( x(t) \) The hidden state \( h(t-1) \), which contains information from the previous steps The RNN essentially performs the same task repeatedly at each step, but it does so by incorporating past data (via the hidden state), making it ideal for sequence data. Time Step Input…