Understanding GRU: A Simple Guide with Examples and Diagrams

Gated Recurrent Units (GRUs) are a special type of recurrent neural network (RNN) designed to capture dependencies in sequential data — such as language, time series, or speech. In this article, we’ll break down GRU in simple language with clear examples and diagrams for every section.

1. Introduction to GRU

GRU is an advanced RNN architecture that helps computers remember important parts of sequences while forgetting less useful information. This makes GRUs especially useful for tasks like language translation, speech recognition, and time-series prediction.

Diagram: Basic RNN with a GRU Unit

Input Sequence
         │
         ▼
   ┌──────────┐
   │  GRU     │  <-- (Processes the sequence, keeping useful info)
   └──────────┘
         │
         ▼
   Output Sequence

Explanation:
In a typical RNN, information from previous inputs is passed along, but GRUs improve this process by using gating mechanisms to decide what to keep or discard. This prevents the network from getting overwhelmed with too much irrelevant information.

2. How GRU Works

A GRU contains two main components (or “gates”) that control its memory: the update gate and the…