✨It’s Day 6/30 – Why Attention Changed Everything! Today I explored how we moved from RNNs to attention-based architectures in NLP. ✨RNNs were decent for short texts, but they compress the entire input into a single hidden state—making it tough to handle long-range dependencies. That’s where the Bahdanau attention mechanism (2014) stepped in. It allowed the decoder to selectively focus on relevant parts of the input sequence at each step, rather than relying on a single compressed representation. ✨This attention mechanism made it possible to model explicit dependencies between different input tokens—enabling the model to decide which words are important for generating each output word. ✨Then came the Transformer architecture, which took it further: No RNNs. Just self-attention, where every word attends to every other word in the sequence—capturing contextual relationships and dependencies in parallel. This shift became the backbone of today’s LLMs like GPT. Next: implementing simp...