Poornima University

LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) are types of Recurrent Neural Networks (RNNs) designed to address the limitations of traditional RNNs. In this explanation, I’ll cover the motivation behind LSTMs and GRUs, their internal architectures, and their differences and similarities, all within the context of improving sequential learning

The vanishing and exploding gradient problem are significant challenges in training Recurrent Neural Networks (RNNs), particularly when working with deep networks or with long sequences of data. This issue arises primarily due to the way backpropagation is conducted through time, and it significantly affects the learning process of RNNs. Let’s explore each of these problems in detail.

It describes mathematical summary of the RNNs. How these works.

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