吴恩达Machine-Learning 课后练习jupyter版本答案 exercise1（系列持续更新）
答案链接：exercise1
https://github.com/NealChalmers/Stanford-CS229-ML-AndrewNg/tree/master/Exercise1
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For this programming exercise, you are only required to complete the first part of the exercise to implement linear regression with one variable. The second part of the exercise, which is optional, covers linear regression with multiple variables. The following is a breakdown of how each part of this exercise is scored.
Required Exercises
Section Part Submitted Function Points
1 Warm up exercise warmUpExercise 10
2 Compute cost for one variable computeCost 40
3 Gradient descent for one variable gradientDescent 50
Total Points 100
Optional Exercises
Section Part Submitted Function Points
4 Feature normalization featureNormalize 0
5 Compute cost for multiple variables computeCostMulti 0
6 Gradient descent for multiple variables gradientDescentMulti 0
7 Normal Equations normalEqn 0
You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.
At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once. They must also be re-executed everytime the submitted function is updated.