Linear Regression and Gradient Descent Quiz

Linear Regression

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

Gradient Descent

• 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.

Stochastic Gradient Descent

• 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.