The ______________ optimization algorithm updates weights more frequently than batch gradient descent by using one training example at a time.

In Stochastic Gradient Descent, each update is noisier than in batch gradient descent, which can be a , but can also help escape

Stochastic gradient descent has fewer amount of computation per gradient update than standard gradient descent.*TrueFalse

Gradient Descent is sometimes referred to as Batch Gradient Descent?1 pointTrue False

The algorithm is known for its efficient computational performance for large datasets by approximating the gradient of the cost function on smaller batches. On the other hand, the algorithm adapts the learning rate for each parameter by considering the recent magnitude of the gradients, helping in faster convergence, especially when dealing with data.

What is the purpose of the learning rate in neural network optimization?Review LaterTo control the number of epochsTo determine the batch sizeTo adjust the step size during weight updatesTo set the initial weights