What is “gradient descent” in machine learning?
a) An optimization algorithm used to minimize the loss function
b) A method to reduce the size of the dataset
c) A technique for splitting data into train and test sets
d) A regularization technique used in neural networks
Answer:
a) An optimization algorithm used to minimize the loss function
Explanation:
Gradient Descent is an optimization algorithm used in machine learning to minimize the loss function by iteratively updating the model parameters. The goal is to find the set of parameters that lead to the lowest possible error on the training data.
The algorithm works by calculating the gradient (derivative) of the loss function with respect to the model’s parameters and updating the parameters in the direction that reduces the loss. This process is repeated until convergence, i.e., when the change in loss becomes negligible.
Gradient Descent is widely used in training machine learning models, especially deep learning models like neural networks, where the objective is to optimize the weights to minimize prediction errors.