Contents

- 1 What is full batch gradient descent?
- 2 Why is stochastic gradient descent better than batch gradient descent?
- 3 What is the difference between online and batch gradient descent?
- 4 What are the benefits of mini batch gradient descent?
- 5 How do you calculate batch gradient descent?
- 6 How does a stochastic gradient descent algorithm work?
- 7 Why is parameter initialization important in gradient descent?

## What is full batch gradient descent?

In Batch Gradient Descent, all the training data is taken into consideration to take a single step. We take the average of the gradients of all the training examples and then use that mean gradient to update our parameters. So that’s just one step of gradient descent in one epoch.

**Is gradient descent and batch gradient descent same?**

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. In the case of Mini-batch Gradient Descent, we take a subset of data and update the parameters based on every subset.

### Why is stochastic gradient descent better than batch gradient descent?

SGD is stochastic in nature i.e it picks up a “random” instance of training data at each step and then computes the gradient making it much faster as there is much fewer data to manipulate at a single time, unlike Batch GD.

**Which of the following is true about batch gradient descent?**

In batch gradient descent we update the weights and biases of our neural network after forward pass over all the training examples is true among the following. Using batch gradient descent, you can train neural networks.

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

Offline learning, also known as batch learning, is akin to batch gradient descent. Online learning, on the other hand, is the analog of stochastic gradient descent. Online learning is data efficient and adaptable. Online learning is data efficient because once data has been consumed it is no longer required.

**Is stochastic gradient descent always faster?**

Stochastic gradient descent (SGD or “on-line”) typically reaches convergence much faster than batch (or “standard”) gradient descent since it updates weight more frequently.

## What are the benefits of mini batch gradient descent?

Mini Batch Gradient Descent Batch : A Compromise

- Easily fits in the memory.
- It is computationally efficient.
- Benefit from vectorization.
- If stuck in local minimums, some noisy steps can lead the way out of them.
- Average of the training samples produces stable error gradients and convergence.

**What are the main benefits of mini batch gradient descent?**

### How do you calculate batch gradient descent?

Gradient descent subtracts the step size from the current value of intercept to get the new value of intercept. This step size is calculated by multiplying the derivative which is -5.7 here to a small number called the learning rate. Usually, we take the value of the learning rate to be 0.1, 0.01 or 0.001.

**How are gradients used in Batch Gradient descent?**

There are different ways in which that man (weights) can go down the slope. Let’s look into them one by one. In Batch Gradient Descent, all the training data is taken into consideration to take a single step. We take the average of the gradients of all the training examples and then use that mean gradient to update our parameters.

#### How does a stochastic gradient descent algorithm work?

Instead of going through all examples, Stochastic Gradient Descent (SGD) performs the parameters update on each example (x^i,y^i). Therefore, learning happens on every example: Shuffle the training data set to avoid pre-existing order of examples. Partition the training data set into m examples.

**When to use SGD for larger datasets?**

SGD can be used for larger datasets. It converges faster when the dataset is large as it causes updates to the parameters more frequently. Mini Batch Gradient Descent We have seen the Batch Gradient Descent.

## Why is parameter initialization important in gradient descent?

Optimization algorithm that is iterative in nature and applied to a set of problems that have non-convex cost functions such as neural networks. Therefore, parameters’ initialization plays a critical role in speeding up convergence and achieving lower error rates.