Gated Recurrent Unit Explained in Tamil | GRU | Deep Learning Tutorial in Tamil | Adi Explains

In this video, we continue our Deep Learning in Tamil series by exploring one of the most important and simplified recurrent neural network architectures used today: the Gated Recurrent Unit, commonly known as GRU. After understanding LSTM in the previous video and learning why long short-term memory networks are powerful for handling long sequences, this session takes you into the world of GRUs, a lighter, faster, and often equally effective alternative to LSTMs. The goal of this video is to help you clearly understand how GRUs work, why they were introduced, and how they solve the same vanishing gradient problems that traditional RNNs struggled with. Everything is explained in simple Tamil so that even beginners can comfortably follow along and build strong intuition. We start with the motivation behind GRUs and why researchers wanted a simpler architecture than LSTM. You will understand how LSTM networks use three gates, and how GRUs reduce this complexity by combining the forget and input gates into a single update gate, making training much more efficient. Throughout the video, the workings of the update gate and reset gate are broken down visually and intuitively, helping you see how these gates control what information to keep, what to forget, and how to generate the next hidden state. By understanding the mathematics behind GRU operations such as element-wise multiplication, candidate hidden states, and gate-controlled updates, you will gain clarity on how GRUs learn long-term dependencies even with fewer parameters. This makes them extremely useful for problems like stock price prediction, sentiment analysis, natural language processing, and any task involving sequential or time-dependent data. As we move deeper into the explanation, you will see how GRUs compare with LSTMs in terms of performance, speed, memory usage, accuracy, and use cases. Many students often wonder whether to choose LSTM or GRU in real projects, so this video aims to remove that conf