Deep Learning · Deep Learning
DL Evaluation and Failures
In Deep Learning because overfit curves and training bugs are the failure modes specific to neural nets.
Most deep learning failures are data or setup bugs.
- Deep Learning
- Medium level
- 3 concepts
1Watch the curves
Plot train versus validation loss; diverging curves scream overfit.
A flat high loss often means learning rate or label bugs, not destiny.
Figure. Read the pair, not either line alone: train falling while validation climbs - the widening gap - screams overfit. Both flat and high is a different disease: a learning rate or label bug, not destiny. The one plot worth drawing after every run.
Both the training and the validation loss sit flat and high from the very first epoch. Is that overfitting?
- Yes, and flat curves are the classic overfit signature
- Yes, and the remedy is more regularisation
- It cannot be diagnosed without also holding out a test set
- No: nothing is being learned at all, which points at the learning rate, the labels or the inputs
Overfitting has a shape: training loss down, validation loss up. Two flat curves are the opposite failure, and it is nearly always a setup bug rather than a hard problem.
2Common bugs
Shuffled labels, leaked IDs, and wrong input scaling create confident nonsense.
Print shapes and a few rows before trusting any neural net.
Figure. Shuffled labels, leaked IDs, and wrong input scaling create confident nonsense. Print shapes and a few rows before trusting any neural net.
A model trained on rows whose labels were accidentally shuffled still reports a low training loss. How can that be?
- Given enough capacity it can memorise the random assignment, which costs nothing on the training set and everything on validation
- It cannot be: shuffled labels always produce a high loss
- The loss function ignores the labels while training
- Shuffling labels is equivalent to shuffling rows, which is harmless
A low training loss proves only that the network could store what it was shown. The gap to validation is what distinguishes a learned pattern from a memorised accident.
3Into applications
You now have neural tools — the tiny vision lesson forces end-to-end judgment on synthetic grids.
Stay on browser-safe tensors; never pull ImageNet into this runtime.
Figure. Neural tools next meet a tiny vision lesson that stays on browser-safe tensors. Do not pull ImageNet into this runtime.
What does an end-to-end lesson ask of you that a single-concept lab does not?
- A larger dataset, since end-to-end implies more data
- Writing the optimizer from scratch instead of importing one
- Choosing the framing, the representation, the model and the metric yourself, where a lab hands you all four
- Running the model outside the browser runtime
The individual steps are the easy part once each has been demonstrated. What is genuinely hard, and what only an end-to-end exercise rehearses, is deciding which step to take next.
Notes
- Overfit, exploding loss, data bugs and metric misuse.
- Plot train versus validation loss; diverging curves scream overfit.
- Shuffled labels, leaked IDs, and wrong input scaling create confident nonsense.
Exam traps & shortcuts
- Keep lab datasets under 2000 rows in the browser runtime.
- Split train and test before fitting any model that sees labels.
Recap
This lesson in brief:
- Watch the curves
- Plot train versus validation loss; diverging curves scream overfit.
- Common bugs
- Shuffled labels, leaked IDs, and wrong input scaling create confident nonsense.
- Into applications
- You now have neural tools — the tiny vision lesson forces end-to-end judgment on synthetic grids.
Practise DL Evaluation and Failures
Reading is free and needs no account. Practice, mocks and progress live in the app.
- A 1-question practice set that ends the chapter
- 3 quick checks with worked explanations
- Timed mocks scored with the real marking scheme
- Readiness tracked per topic, kept on your device