What is the difference between stochastic gradient descent & standard 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.

What is stochastic gradient descent used for?

Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It’s an inexact but powerful technique. Stochastic gradient descent is widely used in machine learning applications.

Does stochastic gradient descent always converge?

Gradient Descent need not always converge at global minimum. It all depends on following conditions; If the line segment between any two points on the graph of the function lies above or on the graph then it is convex function.

In which case the gradient descent algorithm works best?

Gradient descent is best used when the parameters cannot be calculated analytically (e.g. using linear algebra) and must be searched for by an optimization algorithm.

Does SGD use mini batches?

SGD converges faster for larger datasets. But, since in SGD we use only one example at a time, we cannot implement the vectorized implementation on it. We use a batch of a fixed number of training examples which is less than the actual dataset and call it a mini-batch.

Is stochastic gradient descent better than batch gradient descent?

Stochastic gradient descent (SGD or “on-line”) typically reaches convergence much faster than batch (or “standard”) gradient descent since it updates weight more frequently. However, this can also have the advantage that stochastic gradient descent can escape shallow local minima more easily.

How do you use Stochastic Gradient Descent?

How to move down in steps?

1. Find the slope of the objective function with respect to each parameter/feature.
2. Pick a random initial value for the parameters.
3. Update the gradient function by plugging in the parameter values.
4. Calculate the step sizes for each feature as : step size = gradient * learning rate.

What is the Stochastic Gradient Descent Why do we need Stochastic Gradient Descent?

Gradient Descent is the most common optimization algorithm and the foundation of how we train an ML model. But it can be really slow for large datasets. That’s why we use a variant of this algorithm known as Stochastic Gradient Descent to make our model learn a lot faster.

What is gradient descent method will gradient descent methods always converge to the same point?

No, they always don’t. That’s because in some cases it reaches a local minima or a local optima point.

How does gradient descent helps to optimize linear regression model?

Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model.

What is the gradient descent update rule?

The basic equation that describes the update rule of gradient descent is. From this vector, we subtract the gradient of the loss function with respect to the weights multiplied by alpha, the learning rate. The gradient is a vector which gives us the direction in which loss function has the steepest ascent.

Why is SGD used instead of batch gradient descent?

Batch Gradient Descent converges directly to minima. SGD converges faster for larger datasets. But, since in SGD we use only one example at a time, we cannot implement the vectorized implementation on it. This can slow down the computations.