Evaluating fine-tuned models: avoiding overfitting

Evaluating fine-tuned models: overfitting, forgetting and benchmarks

Training a model is easy. Knowing when to stop, detecting that it is memorising instead of learning, and verifying that your specialisation did not break general capabilities — these are the hard parts. This lesson covers the diagnostic tools that tell you whether your fine-tuning is working, going too far, or silently degrading the model.

Full content is available with a subscription.

Get full access to all courses on the platform for one year with a single payment.

Unlike other platforms that charge per course, here you get everything for one price, and after one year of use there will be no automatic charge for the following year.