Q1) : For a 3 dimensional dataset, What is the minimum number of points that are required to t a hyperplane?

Q2) What is the difference between deep and shallow learning. Explain with concrete example(s) when shallow learning is suitable as compared to deep learning and vise versa

Q3) answer these questions using python code

- implement gradient decent algorithm to learn parameters of a linear neuron using squared error objective function. The weights will be the coecients of a linear regression model along with the bias.

- Visualize the learned model (plot both datasets separately along with learnt line).

- Report the coecient of the model along with the bias.

- Report model's performance using Mean Squared Error.

- Write a funtion which accepts the radius of a circle as an argument and returns the area.

- Write a function in python that takes the address of a directory, and the address of a text le, and a le extension (e.g. '.jpg') as inputs. The function nds what les are in the directory with the input extension, and writes their names in that text le. If there are no les with this extension, it should write in the le "no les with extension (replace with input extension, here) "