start with the idea before the implementation.
the mechanisms you need to reason about.
Layers and activations
Layers and activations is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Backpropagation
Backpropagation is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Initialization
Initialization is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Training curves
Training curves is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
turn the lesson into evidence.
train a small MLP
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
inspect train/validation loss
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
change width and regularization
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
prove you can explain and decide.
explain gradient flow
ask cortex to test me →diagnose overfitting
ask cortex to test me →choose an activation
ask cortex to test me →what usually goes wrong.
too much capacity
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
no baseline
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
learning-rate instability
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
Create a short MLP engineering note with one working artifact one metric one failure case and one decision about when you would or would not use it.
Save the result in your portfolio or project repository. A strong learning artifact should make your assumptions, metrics and failure analysis visible.