Contents

- 1 What is batch in gradient descent?
- 2 Why is batch gradient descent expensive?
- 3 Is SGD faster than batch gradient descent?
- 4 What is the difference between SGD and gradient descent?
- 5 What is the difference between batch and stochastic gradient descent?
- 6 What are the weaknesses of gradient descent?
- 7 How does mini-batch gradient descent work?
- 8 Can you please explain the gradient descent?
- 9 How to calculate gradient in gradient descent?

## What is batch in gradient descent?

Batch gradient descent is a variation of the gradient descent algorithm that calculates the error for each example in the training dataset, but only updates the model after all training examples have been evaluated. One cycle through the entire training dataset is called a training epoch.

## Why is batch gradient descent expensive?

Batch Gradient Descent: Batch Gradient Descent involves calculations over the full training set at each step as a result of which it is very slow on very large training data. Thus, it becomes very computationally expensive to do Batch GD.

## Is SGD faster than batch gradient descent?

SGD can be used when the dataset is large. Batch Gradient Descent converges directly to minima. SGD converges faster for larger datasets. We use a batch of a fixed number of training examples which is less than the actual dataset and call it a mini-batch.

## What is the difference between SGD and gradient descent?

The only difference comes while iterating. In Gradient Descent, we consider all the points in calculating loss and derivative, while in Stochastic gradient descent, we use single point in loss function and its derivative randomly. Check out these two articles, both are inter-related and well explained.

## What is the difference between batch and stochastic gradient descent?

In batch gradient Descent, as we have seen earlier as well, we take the entire dataset > calculate the cost function > update parameter. In the case of Stochastic Gradient Descent, we update the parameters after every single observation and we know that every time the weights are updated it is known as an iteration.

## What are the weaknesses of gradient descent?

Weaknesses of Gradient Descent: The learning rate can affect which minimum you reach and how quickly you reach it. If learning rate is too high (misses the minima) or too low (time consuming) Can…

## How does mini-batch gradient descent work?

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.

## Can you please explain the gradient descent?

Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. The idea is to take repeated steps in the opposite direction of the gradient (or approximate gradient) of the function at the current point, because this is the direction of steepest descent. Conversely, stepping in the direction of the gradient will lead to a local

## How to calculate gradient in gradient descent?

How to understand Gradient Descent algorithm Initialize the weights (a & b) with random values and calculate Error (SSE) Calculate the gradient i.e. change in SSE when the weights (a & b) are changed by a very small value from their original randomly initialized value. Adjust the weights with the gradients to reach the optimal values where SSE is minimized