Gated Recurrent Units (GRUs) are an efficient alternative to LSTMs for sequential data modeling. This in-depth guide explains why GRUs exist, how their reset and update gates control memory, and walks through detailed numerical examples and intuitive analogies to help you truly understand how GRUs work internally.

LSTM (Long Short-Term Memory) is a powerful neural network architecture designed to handle long-term dependencies in sequential data. In this blog, we explain LSTMs intuitively using a simple story, compare them with traditional RNNs, and break down forget, input, and output gates in a clear, beginner-friendly way.