Backpropagation
Backpropagation in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
Beyond linear boundaries
Use beyond linear boundaries to move the neural networks production brief toward a defensible release.
Neural networks from scratchForward pass, activations, losses, and initialization
Use forward pass, activations, losses, and initialization to move the neural networks production brief toward a defensible release.
Neural networks from scratchBackpropagation without magic
Use backpropagation without magic to move the neural networks production brief toward a defensible release.
Neural networks from scratchSGD, momentum, AdamW, and schedules
Use sgd, momentum, adamw, and schedules to move the neural networks production brief toward a defensible release.
Neural networks from scratchRegularize, normalize, debug, and ship
Use regularize, normalize, debug, and ship to move the neural networks production brief toward a defensible release.
Neural networks from scratchActivation functions: geometry, gradients, and failure modes
Select activation functions by signal range, gradient flow, architecture, precision, and failure mode; then diagnose saturation and dead units from telemetry.
Neural networks from scratchLoss functions by task, data, and deployment constraints
Choose and debug training objectives as optimization surrogates for the real task, including imbalance, outliers, uncertainty, ranking, dense prediction, and generation.
Courses & assignments
Dependency-authoritative learning units.
Casebook
Reported facts and course reconstructions.