吴恩达Machine-Learning 课后练习jupyter版本答案 exercise3（系列持续更新）
答案链接：exercise3
https://github.com/NealChalmers/Stanford-CS229-ML-AndrewNg/tree/master/Exercise3
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Multi-class Classification and Neural Networks
Introduction
In this exercise, you will implement one-vs-all logistic regression and neural networks to recognize handwritten digits. Before starting the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.
All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).
Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using numpy for all arrays and matrix operations, matplotlib for plotting, and scipy for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the github repository.
Section Part Submission function Points
1 Regularized Logistic Regression lrCostFunction 30
2 One-vs-all classifier training oneVsAll 20
3 One-vs-all classifier prediction predictOneVsAll 20
4 Neural Network Prediction Function predict 30
Total Points 100
You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.