Simple RNN

Simple RNN (Recurrent Neural Network) is a foundational type of neural network architecture designed for processing sequential data, such as time series, text, or speech. It maintains a hidden state that captures information from previous time steps, allowing it to model temporal dependencies by passing this state forward through the network. However, it suffers from issues like vanishing gradients, which limit its ability to learn long-range dependencies effectively.

Also known as: Vanilla RNN, Basic RNN, Standard RNN, Elman Network, Simple Recurrent Neural Network

Why learn Simple RNN?

Developers should learn Simple RNNs when working on tasks involving sequential data, such as natural language processing (e.g., text generation or sentiment analysis) or time series prediction (e.g., stock prices or weather forecasting). It serves as a starting point for understanding more advanced RNN variants like LSTM and GRU, which address its limitations, making it essential for building foundational knowledge in deep learning for sequences.

Learning Resources

• course: Understanding Simple RNNs - DeepLearning.AI
• tutorial: Simple RNN Tutorial - TensorFlow
• docs: Recurrent Neural Networks (RNN) - Stanford CS231n
• video: Simple RNN Explained - YouTube by StatQuest