Why do we use mini batch gradient descent?
Mini-batch gradient descent is a variation of the gradient descent algorithm that splits the training dataset into small batches that are used to calculate model error and update model coefficients. Implementations may choose to sum the gradient over the mini-batch which further reduces the variance of the gradient.
What is the difference between mini batch and stochastic gradient descent?
When the batch is the size of one sample, the learning algorithm is called stochastic gradient descent. When the batch size is more than one sample and less than the size of the training dataset, the learning algorithm is called mini-batch gradient descent.
How is mini-batch gradient descent used in training?
Mini-batch gradient descent is a variation of the gradient descent algorithm that splits the training dataset into small batches that are used to calculate model error and update model coefficients. Implementations may choose to sum the gradient over the mini-batch which further reduces the variance of the gradient.
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 to calculate gradient in stochastic gradient descent?
We do the following steps in one epoch for SGD: Feed it to Neural Network Calculate it’s gradient Use the gradient we calculated in step 3 to update the weights Repeat steps 1–4 for all the examples in training dataset
When to update parameters in Batch Gradient descent?
Batch Gradient Descent: Parameters are updated after computing the gradient of error with respect to the entire training set Stochastic Gradient Descent: Parameters are updated after computing the gradient of error with respect to a single training example