Topic overview
Study layers, activations, loss functions, backpropagation, optimization, regularization, CNNs, RNNs, and transformers.
Core concepts
Gradient descent, embeddings, attention, normalization, initialization, overfitting, transfer learning, and training dynamics.
Why it matters
Deep learning powers many modern AI systems, but reliability depends on data, evaluation, and deployment discipline.
Interview relevance
Interviewers expect conceptual clarity, not just framework usage: explain why architectures and metrics fit the problem.