Ever wondered how Google Translate really works and wanted to create something like it? In this blog post, we embark on a journey to build a Transformer-based translation system inspired by the landmark “Attention Is All You Need” paper, illuminating self-attention, multi-head attention, positional encodings, and the encoder–decoder stack through a hands-on PyTorch implementation trained on the massive Samanantar English–Indic corpus, showing you how to tune epochs, batch size, and learning rate, deploy a Streamlit demo that mirrors Google Translate, and trace the Transformer’s rise from its RNN and LSTM predecessors.
