Transformer Architecture in Tamil | Encoder Decoder & Attention Explained | Deep Learning NLP

In this video, we dive deep into Transformer Architecture explained in Tamil, one of the most important and widely used architectures in Deep Learning, NLP, and Generative AI today. This video is a part of our Deep Learning Full Series in Tamil, designed specifically for learners who want to understand complex AI concepts in a simple, intuitive, and practical way using Tamil language. If you are a college student preparing for placements, a working professional upskilling in AI, or someone curious about how modern models like ChatGPT, Google Translate, and BERT work internally, this video is made for you. This video explains the Transformer model step by step, without skipping fundamentals. Instead of memorizing formulas, we focus on why each component exists and how it works internally. To make the concept crystal clear, we explain the entire transformer architecture using a real-world example of English to Tamil sentence translation. By the end of this video, you will clearly understand how a sentence is taken as input, processed through the encoder and decoder, and finally converted into a meaningful Tamil translation. We start with the input embeddings, explaining how words are converted into vectors and why embeddings are necessary for neural networks. Then we move into positional encoding, where you’ll understand how transformers capture word order without using RNNs or LSTMs. From there, we explain self-attention in Tamil, showing how each word attends to other words in the sentence and why attention is the heart of the transformer architecture. Next, we break down multi-head attention, explaining why multiple attention heads are used instead of just one and how they help the model learn different relationships like grammar, meaning, and context at the same time. We then explain residual connections and layer normalization, clarifying how they help with stable training, faster convergence, and solving vanishing or exploding gradient problems in deep neural