Theoretical derivations and concrete implementations of deep learning primitives from scratch.
Mathematical derivation of error delta terms, output layer gradients, hidden layer backpropagation, and weight/bias updates using the chain rule.
Practical browser simulations and numerical benchmarks validating the mathematical theories discussed in this course: