A clear explanation of how RNNs work and why they struggle with long-term dependencies, followed by a technical walkthrough of how LSTM's gating mechanisms solve the vanishing gradient problem.

A beginner-friendly explanation of embedding layers in deep learning, showing how discrete data like words are transformed into meaningful numerical vectors that allow neural networks to learn semantic relationships.