How do you train artificial intelligence?

20 Tem 01:37

Training data isn't as simple as you might think, because just having images isn't enough. For example, a dataset for an image recognition model shouldn't just include high-resolution photos—it should also feature varying lighting conditions, angles, noise, and even changes in the background. If you're trying to train a model to recognize a cat, the dataset should include photos of cats in different poses, breeds, and even images where the cat is shadowed or partially obscured by other objects. This way, the model doesn’t just focus on the label "cat" but learns to identify the object’s features. This is where overfitting comes into play. Imagine you have a dataset of just 10 photos, all taken in the same way with the same background. The model might perform perfectly on this tiny dataset, but it’ll fail in the real world because it can’t adapt to the diversity out there. Overfitting means the model has "learned too well" from the training data, but this usually kills its ability to generalize. How do you spot it? If your model achieves 99% accuracy on the training data but drops to 60% on the test data, that’s a clear sign of overfitting.