Machine Learning Fundamentals

What is the primary goal of supervised learning in machine learning?

Which of the following best explains why overfitting occurs in neural networks?

How does gradient descent help in training a neural network?

You train a neural network on a small dataset and notice the training accuracy is very high but the validation accuracy is low. What is the most likely cause?

Which strategy can help reduce overfitting in neural networks during supervised learning?

If the gradient computed during gradient descent is very small, what can be a practical consequence?

In supervised learning, the training data includes input-output pairs.

Gradient descent always finds the global minimum of the loss function in neural networks.

Overfitting means a model performs well on training data but poorly on new unseen data.