UNLOCKING RNN, Layer Normalization, and LSTMs – Mastering the Depth of RNNs in Deep Learning – Part 8 of RNN Series by INGOAMPT – Day 62

A Deep Dive into Recurrent Neural Networks, Layer Normalization, and LSTMs So far we have explained in pervious days articles a lot about RNN. We have explained, Recurrent Neural Networks (RNNs) are a cornerstone in handling sequential data, ranging from time series analysis to natural language processing. However, training RNNs comes with challenges, particularly when dealing with long sequences and issues like unstable gradients. This post will cover how Layer Normalization (LN) addresses these challenges and how Long Short-Term Memory (LSTM) networks provide a more robust solution to memory retention in sequence models. The Challenges of RNNs: Long Sequences and Unstable Gradients When training an RNN over long sequences, the network can experience the unstable gradient problem—where gradients either explode or vanish during backpropagation. This makes training unstable and inefficient. Additionally, RNNs may start to “forget” earlier inputs as they move forward through the sequence, leading to poor retention of important data points, a phenomenon referred to as the short-term memory problem. Addressing Unstable Gradients: Gradient Clipping: Limits the maximum value of gradients, ensuring they don’t grow excessively large. Smaller Learning Rates: Using a smaller learning rate helps prevent gradients from overshooting during updates. Activation Functions: Saturating activation functions like…