# Linear Regression and Gradient Descent Quiz

TriumphantTensor
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## 11 Questions

### What is the main goal of linear regression according to the given text?

To predict a continuous value

Stochastic

### What is a common issue that arises when the learning rate is too large as mentioned in the text?

Oscillation and potentially diverging

### What aspect of gradient descent does the learning rate control?

The size of the step taken at each iteration

### Which statement about the analytical solution to linear regression is accurate based on the text?

It can be computationally expensive for large datasets

### In gradient descent, how does momentum affect the process as described in the text?

By adding a fraction of the previous step to the current step

### What is a potential drawback of using mini-batch in Stochastic Gradient Descent?

It may increase the noise in the gradient estimate

To balance the speed of convergence and the risk of overshooting

### In Stochastic Gradient Descent, what does the term 'mini-batch' refer to?

Using a subset of the dataset for each iteration

### What characteristic is NOT associated with mini-batch in Stochastic Gradient Descent?

Finding the global minimum

## Study Notes

### Linear Regression

• The main goal of linear regression is to find the best-fitting linear line that minimizes the sum of the squared errors.

• Stochastic Gradient Descent (SGD) with online learning uses a single example per iteration.
• If the learning rate is too large, it can cause oscillations and fail to converge.
• The learning rate controls how quickly the model learns from new data.
• Momentum in gradient descent helps the process by adding a fraction of the previous weight update to the current update, helping to escape local minima.

### Analytical Solution

• The analytical solution to linear regression involves minimizing the cost function using normal equations, which have a closed-form solution.

• Mini-batch in Stochastic Gradient Descent refers to a subset of the training data used to compute the gradient of the loss function.
• A potential drawback of using mini-batch is that it can still be computationally expensive.
• Adjusting the learning rate is necessary to ensure convergence and avoid oscillations.

### Optimization Algorithms

• The Adam optimization algorithm automatically adjusts learning rates for different parameters.

### Mini-Batch Characteristic

• Mini-batch is not associated with full-batch gradient descent, which uses the entire training dataset to compute the gradient.

Test your knowledge on linear regression and gradient descent concepts with this quiz. Questions cover topics such as the goal of linear regression, gradient descent variants, and common issues related to learning rates.

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