Which optimization algorithm adapts the learning rate for each parameter based on its gradient history?Group of answer choicesGradient DescentStochastic Gradient Descent (SGD)AdaGradRMSprop

Gradient Descent is an optimization algorithm used for ______

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.

Which of the following statements about Gradient Descent are true? (Select all that apply)Group of answer choicesLearning rate is a crucial hyperparameter in its performance.It requires calculation of gradients for the entire dataset.It can be very slow when the dataset is very large.It is guaranteed to find the global minimum for non-convex functions.

What is a key feature of AdaDelta compared to other gradient descent algorithms?Group of answer choicesIt calculates momentum to speed up the learning process.It adjusts the learning rate per parameter based on a moving window of gradient updates.It is a first-order optimization algorithm that relies on first derivatives.It requires a manually set learning rate.

How are gradient descent and learning rate used in logistic regression? 1 pointGradient descent takes increasingly bigger steps towards the minimum with each iteration.Gradient descent will minimize learning rate to minimize the cost in fewer iterations.Gradient descent specifies the steps to take in the current slope direction, learning rate is the step length.We want to minimize the cost by maximizing the learning rate value.