Working notes from applied deep-learning practice.
October 26, 2026 · LayerGenius
Most augmentation pipelines are copied from a tutorial and never measured. Here is how we pick transforms from the failure modes in the data, place them so they don't starve the GPU, and ablate them one at a time.
Read moreOctober 19, 2026 · LayerGenius
A single validation number tells you almost nothing about what to fix. How we run error analysis on a Keras classifier: per-slice metrics, a ranked confusion inventory, Grad-CAM as a hypothesis test, and the rule for deciding between a data fix and a model change.
Read moreOctober 12, 2026
Quantization is the first compression lever, not the only one. Structured pruning, magnitude sparsity, and knowledge distillation in Keras 3 — what each one actually buys on real hardware, and how to measure it.
Read moreOctober 5, 2026
Most Keras bugs are software bugs, not modeling bugs. A small pytest suite — shape and dtype contracts, overfit-one-batch, leakage checks, serialization round-trips — catches them before a GPU run does.
Read moreSeptember 28, 2026 · LayerGenius
Most stalled Keras projects are data problems wearing a modeling costume. Out-of-fold confident-learning scans, embedding-based duplicate detection, and a fix-the-test-set-first policy — with the numbers to justify the hours.
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