Learning objectives
- • Explain gradient descent and backpropagation step by step
- • Compare SGD, momentum, RMSProp, and Adam
- • Diagnose vanishing gradients and unstable training
Deep Learning
Backpropagation and Optimization
Build an interview-safe explanation for gradient flow, learning dynamics, and optimization choices.
Recommended on day 24110 minutesIntermediate
Interview prompts
- • Why does Adam often converge faster but sometimes generalize worse?
- • What role does normalization play in deep networks?
Prerequisites