Module
Introduction to Neural Networks
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About
Multilayer perceptrons as composition of linear + nonlinear layers; forward pass, backpropagation intuition/chain rule, training with SGD, and connection to logistic regression as a one-layer net. Overfitting controls (weight decay, early stopping, simple dropout mention).
Goal
Implement a small MLP (1–2 hidden layers) in NumPy or a minimal framework; train on a toy or small real dataset with proper splits; use regularization/early stopping; explain forward and backward passes at a high level.
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