www.pickl.ai
Architecture, Advantages, and Applications
An RNN consists of: – Input layer – Hidden layers with recurrent connections – Output layer
The hidden state in RNNs acts as memory, storing information from previous inputs.
– Natural Language Processing – Speech recognition – Sentiment analysis – Generating sequential data like music or text.
It can learn temporal dependencies in data
Struggle with long-range dependencies
Advanced models like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) enhances memory retention and performance in complex tasks.