start with the idea before the implementation.
the mechanisms you need to reason about.
Self-attention
Self-attention 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.
Positional information
Positional information 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.
Residuals and normalization
Residuals and normalization 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.
Encoder vs decoder
Encoder vs decoder 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.
trace one transformer block
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
calculate attention complexity
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
compare encoder and decoder use cases
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
prove you can explain and decide.
explain causal decoding
ask cortex to test me →identify memory bottlenecks
ask cortex to test me →describe residual paths
ask cortex to test me →what usually goes wrong.
context-length cost
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
training-data dependence
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
confusing architecture with intelligence
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 Transformers 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.