RNN in Tamil | Recurrent Neural Networks Explained | Deep Learning Tamil Tutorial

Learning Recurrent Neural Networks (RNN) is a must for anyone who wants to master deep learning, especially when working with sequential data such as text, speech, and time series. In this video, explained completely in Tamil, we go step by step into the world of RNNs and make sure you understand not only the intuition but also the technical details. This lesson is part of the Deep Learning in Tamil series by Adi Explains, created especially for Tamil learners who want to gain strong foundations in machine learning and artificial intelligence. We begin by introducing what makes RNNs unique compared to traditional feedforward neural networks. Regular neural networks treat inputs as independent, but RNNs are designed for sequence modeling. By maintaining hidden states across time, they are able to capture dependencies and context between inputs. This makes RNNs essential for natural language processing tasks, speech recognition, and other sequence-related problems. The video explains the concepts of input, hidden states, and output in Tamil so learners can build clarity step by step. The session also covers different sequence modeling architectures with clear examples. You will learn about many-to-one models, such as those used in sentiment analysis where a sequence of words leads to a single classification. We then move on to one-to-many models where one input generates a sequence of outputs, like image captioning. The video also explains sequence-to-vector and vector-to-sequence models, which are key in tasks like machine translation and summarization. By understanding these variations, you will see how RNNs are applied in real-world scenarios. For many Tamil learners, the biggest challenge is grasping how data flows through RNNs at each time step. That’s why this video focuses on explaining the input, hidden state, and output process visually and conceptually in Tamil. You will see how past information is carried forward and how the network learns from dependenc