Contents
Is grid search faster than random search?
Once again, the Grid Search outperformed the Random Search. This is most likely due to the small dimensions of the data set (only 2000 samples). With larger data sets, it’s advisable to instead perform a Randomized Search.
Why is it called grid search?
The name “grid” comes to the fact that all possible candidates within all needed hyperparameters are combined in a sort of grid. The combination yielding the best performance, preferably evaluated in a validation set, is then selected.
What is grid search for Hyperparameter tuning?
Grid search is the simplest algorithm for hyperparameter tuning. Basically, we divide the domain of the hyperparameters into a discrete grid. Then, we try every combination of values of this grid, calculating some performance metrics using cross-validation.
Are there any problems with the gradient descent algorithm?
A problem with the gradient descent algorithm is that the progression of the search can bounce around the search space based on the gradient.
How is stochastic gradient descent used in machine learning?
Stochastic Gradient Descent is the extension of Gradient Descent. Any Machine Learning/ Deep Learning function works on the same objective function f (x) to reduce the error and generalize when a new data comes in.
How is gradient descent accelerated by using momentum?
Gradient descent is an optimization algorithm that uses the gradient of the objective function to navigate the search space. Gradient descent can be accelerated by using momentum from past updates to the search position. How to implement gradient descent optimization with momentum and develop an intuition for its behavior.
How is mini-batch gradient descent used in deep learning?
Mini-batch gradient descent seeks to find a balance between the robustness of stochastic gradient descent and the efficiency of batch gradient descent. Mini-batch gradient descent is the most common implementation of gradient descent used in the field of deep learning.