What is recurrent?

A recurrent neural network (RNN) is a type of artificial intelligence model designed to work with data that comes in sequences, like sentences, audio clips, or stock prices. Unlike regular neural networks, an RNN has connections that loop back on themselves, allowing it to keep a “memory” of previous information while processing new data.

Let's break it down

  • Input layer: receives one piece of the sequence at a time (e.g., one word).
  • Hidden state: a set of numbers that stores information from earlier steps; it gets updated at each step.
  • Output layer: produces a result for the current step (e.g., the next word prediction).
  • Time steps: the network processes the sequence step‑by‑step, sharing the same weights at every step, which makes it efficient for long inputs.

Why does it matter?

Because many real‑world problems involve order and context, RNNs let computers understand and generate things like language, music, or sensor data. Their ability to remember past information makes them essential for tasks where the meaning of something depends on what came before.

Where is it used?

  • Language modeling & text generation (e.g., autocomplete, chatbots)
  • Machine translation (converting sentences from one language to another)
  • Speech recognition (turning spoken words into text)
  • Music composition (creating new melodies)
  • Time‑series forecasting (predicting stock prices, weather, etc.)

Good things about it

  • Handles inputs of varying length without needing a fixed size.
  • Captures temporal relationships and context naturally.
  • Uses the same parameters at each time step, reducing the total number of weights.
  • Can be trained end‑to‑end for many different sequence tasks.

Not-so-good things

  • Prone to “vanishing” or “exploding” gradients, making it hard to learn long‑range dependencies.
  • Training can be slower and more complex than some newer models.
  • Often outperformed by Transformer‑based architectures on large language tasks.
  • Requires careful tuning of hyperparameters and sometimes additional tricks (e.g., LSTM or GRU cells) to work well.