Day 172 — Recurrent Neural Networks and LSTMs

What We Are Building Today
By the end of this lesson you will have done three things that actually matter:

Understood why sequential data breaks standard networks — and what RNNs do differently at the architectural level.
Decoded how LSTMs solve the vanishing gradient problem that crippled early RNNs, using three learned memory gates.
Built a working character-level language model that generates text one character at a time — the same core idea behind every autocomplete you have ever used.
Connected this to production systems at Google, Amazon, Apple, and Stripe that run LSTM-based models today.

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