1.
A sequence of steps used to solve a problem or perform a computation
2.
A structured collection of data used for training or testing models.
3.
Pertaining to network architectures inspired by the brain.
4.
Process of validating model performance and correctness.
5.
A controlled procedure to test a hypothesis.
6.
To shuffle or assign unpredictably for unbiased results.
7.
True output values used for supervised learning.
8.
To improve model performance by minimizing error.
9.
Algorithm type inspired by biological evolution.
10.
Quantitative measures used to evaluate models.
11.
Phase where a model learns from data.
12.
The field focused on building intelligent systems.
