15-minute session: read for 3 minutes, trace for 3, experiment for 7, and complete the check for 2. Spend longer whenever you need.
A model describes parameters and operations. A runtime loads that representation, validates shapes and types, allocates memory, schedules operations, and executes kernels. Training and inference are different workflows. Begin with a tiny deterministic computation so correctness can be checked by hand before introducing acceleration.
Practice and evidence: Predict the example or draw its state transitions. Change one assumption, test the boundary, and record the actual result. Explain what this small model does not establish about a production system.
x=[2.0,3.0]
w=[0.5,2.0]
bias=1.0
y=sum(a*b for a,b in zip(x,w))+bias
print(y)
Expected result: 8.0
Keep three notes: your prediction, the actual result, and why they match or differ. The completion check grades understanding; the practice task builds skill.