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. 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, RNN

Why learn Simple RNN?

Developers should learn Simple RNN as an introductory concept to understand how neural networks handle sequential data, making it useful for basic tasks like time series prediction or simple natural language processing. It serves as a stepping stone to more advanced architectures like LSTM or GRU, which address its limitations in real-world applications such as machine translation or speech recognition.

Learning Resources

• tutorial: Understanding LSTM Networks
• course: Deep Learning Specialization on Coursera
• tutorial: PyTorch RNN Tutorial
• docs: TensorFlow RNN Guide
• book: Recurrent Neural Networks (RNN) - Deep Learning Book