Deep Learning / neural-network-foundations

Neural network foundations

Perceptrons, MLPs, computational graphs, backprop, activations, initialization, optimizers, and regularization.

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Focus
  • forward and backward pass
  • activation functions
  • dropout and weight decay
  • AdamW
Future feature idea

Forward/backprop visualizer with activations, loss, dropout, initialization, and optimizer choices.

Self-check
  1. What is the core job of "Neural network foundations"?
  2. Which common mistake would break a production implementation of this topic?
  3. Which inputs or limits must be validated before the interactive feature ships?
  4. What is the smallest test that proves the future implementation behaves correctly?
  5. When does this module really need backend compute, and when is a UI simulation enough?
Implementation notes
  • Start with one focused feature, not a full course inside one page.
  • All public inputs must be typed, bounded, and covered by reject-case tests.
  • If a model, dataset, or job is added, document source, license, limits, and fallback.
  • The interaction must explain the topic rather than serve as decoration.