Selçuk Korkmaz
@selcukorkmaz.bsky.social
Passionate about data & innovation | Building tools to simplify complex problems | Advocate for open-access research & AI-driven solutions | https://selcukorkmaz.github.io/
8/8 In short:
Leak-free design
Real signal
Correct inductive bias
Distribution alignment
Appropriate loss and algorithm
Without these, high performance is only an illusion.
Leak-free design
Real signal
Correct inductive bias
Distribution alignment
Appropriate loss and algorithm
Without these, high performance is only an illusion.
November 29, 2025 at 5:28 PM
8/8 In short:
Leak-free design
Real signal
Correct inductive bias
Distribution alignment
Appropriate loss and algorithm
Without these, high performance is only an illusion.
Leak-free design
Real signal
Correct inductive bias
Distribution alignment
Appropriate loss and algorithm
Without these, high performance is only an illusion.
7/8 Finally, hyperparameter tuning is just optimization. The true structure of the model is determined by the loss function and the algorithm. Tuning cannot fix a fundamentally wrong design.
November 29, 2025 at 5:28 PM
7/8 Finally, hyperparameter tuning is just optimization. The true structure of the model is determined by the loss function and the algorithm. Tuning cannot fix a fundamentally wrong design.
6/8 Another core assumption is distribution alignment: the data the model sees during training must come from the same distribution as the data it will face in reality. If these differ, errors are inevitable.
November 29, 2025 at 5:28 PM
6/8 Another core assumption is distribution alignment: the data the model sees during training must come from the same distribution as the data it will face in reality. If these differ, errors are inevitable.
5/8 When this bias matches the problem, the model generalizes well. When it doesn’t, the model may look perfect during training but fail immediately in practice. Capacity control, regularization, and appropriate architecture choices are the main tools to manage this.
November 29, 2025 at 5:28 PM
5/8 When this bias matches the problem, the model generalizes well. When it doesn’t, the model may look perfect during training but fail immediately in practice. Capacity control, regularization, and appropriate architecture choices are the main tools to manage this.
4/8 Third, inductive bias. This is the model’s built-in view of how the world works.
Decision trees assume sharp splits.
Linear models assume linear relationships.
Deep learning assumes the ability to represent highly complex structures.
Decision trees assume sharp splits.
Linear models assume linear relationships.
Deep learning assumes the ability to represent highly complex structures.
November 29, 2025 at 5:28 PM
4/8 Third, inductive bias. This is the model’s built-in view of how the world works.
Decision trees assume sharp splits.
Linear models assume linear relationships.
Deep learning assumes the ability to represent highly complex structures.
Decision trees assume sharp splits.
Linear models assume linear relationships.
Deep learning assumes the ability to represent highly complex structures.
3/8 Second, the data must contain real signal. There must be a learnable relationship. If noise dominates or the sample size is too small, even the best algorithm fails. Models cannot learn from noise.
November 29, 2025 at 5:28 PM
3/8 Second, the data must contain real signal. There must be a learnable relationship. If noise dominates or the sample size is too small, even the best algorithm fails. Models cannot learn from noise.
2/8 The first requirement is a leak-free experimental design. No information from the test set should leak into the training process. If leakage exists, the model isn’t performing, it’s just seeing the answers beforehand.
November 29, 2025 at 5:28 PM
2/8 The first requirement is a leak-free experimental design. No information from the test set should leak into the training process. If leakage exists, the model isn’t performing, it’s just seeing the answers beforehand.
