Description
. Your task is to find a linear approximation of the function 1 + x, x ∈ [0,1]. Your homework should contain the following steps:
- Generate N = 10000 random numbers from [0,1]:
x1,x2,…,xN ∈ [0,1],
√
and then obtain their labels: yi = 1 + xi, i = 1,2,…,N.
- Do linear regression on your generated data using the closed formsolution.
- Do linear regression on your generated data using the library sklearn.
- Do linear regression on your generated data implementing the gradi-ent descent algorithm by yourself. e*) Do linear regression on your generated data using tensorflow.
- Sketch the graphs of all approximations on one graph.
- Compare all solutions with the first degree Taylor approximation of√
the function 1 + x.
2*. a) How will you define polynomial regression inspired from linear regression?
- b) Can you implement the polynomial regression using linear regression?
Remarks:
- Exercises with asterisks are supplementary and will not be graded.
- Don’t forget about train, validation and test sets.
- Use jupyter notebook for writing your code.
- You can use google for any question, but don’t do copies of others’ codes.
- You can ask me whatever you want and whenever you want.





