Temperature Scaling
A single parameter that softens or sharpens a network’s probabilities without touching which class it picks — and what that single parameter cannot fix.
-
-
Neural Networks
Neural networks learn parameterized transformations. Layers, nonlinearities, loss, gradients, and optimization play different roles in fitting a useful model.
-
Logits
The logits [2, 1, 0] and [102, 101, 100] produce identical probabilities of [0.6652, 0.2447, 0.0900]. Only the gaps between them are identified.
-
Neural Network Forward Propagation
A beginner-friendly explanation of neural-network structure, notation, hidden layers, and forward propagation with a verified NumPy example.
-
NumPy Vectorization and Broadcasting
Replace slow loops with clear matrix operations and learn how to prevent silent broadcasting and shape errors in NumPy.