Training Dynamics & Regularisation Clinic
P6.dl-architectures.10 · Audience: guest, language-pro, it-ml · Prerequisites: Regularisation and Normalisation
Everything in this track taught you a mechanism — backprop, SGD, Adam,
dropout. The clinic teaches the craft: reading a run's behaviour and
deciding what to change. This is the capstone of Deep-Learning Architectures,
and it ends with the platform's first real PyTorch training rungs, graded
in the deep-learning kernel.
ⓘ Concept: The four silhouettes every practitioner knows
- Healthy: train and validation fall together, then flatten. Ship it.
- Overfitting: train keeps falling, validation turns upward — the module-05 story. Regularise or stop early.
- Diverging: the loss jumps up and pins at a ceiling, or explodes to NaN. Almost always the learning rate.
- Stuck: the loss flatlines high from step one — a dead architecture, a zero learning rate, or a bad initialisation (a local minimum).
Why it matters — A loss curve is the run's vital sign. Recognising its silhouette in the first seconds tells you what to try next — before you waste an hour of compute.
Replay the overfitting silhouette from module 03 — move the overfit-start slider and watch the validation curve turn:
Ask the mentor about this module
Ask a question about this content. The mentor explains and grounds its answer in what you are studying; asking is recorded as a learning signal, not a grade.
🎓 Practice ladder
3 graded rungs · ~37 minThe capstone ladder. Rung 1 runs in your browser; rungs 2 and 3 are the
platform's first PyTorch rungs — your code executes
server-side in the sandboxed deep-learning kernel (CPU torch,
metered, no network).
Rung 1 — One SGD step, one Adam step (by hand)
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Rung 2 — Train a tiny torch MLP on XOR (kernel)
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Rung 3 — Diagnose the diverging run (kernel)
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Try it yourself
A scratch console for this page's ideas — ungraded, nothing you run here is recorded.
Scratch console
A scratch console with the scientific stack (pandas, numpy, scikit-learn). Runs on the server — no network, resource-limited and measured.
Output appears here.