Coursera Machine Learning 第六周编程week6 ex5Regularized Linear Regression and BiasVariance编程全套满分题目+注释选做
2021-09-01 11:00:31
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因为吴恩达老师深度学习课程第四课第三周作业中需要用到yolo.h5，于是自己利用yolov2.cfg与yolov2.weights自己生成的yolo.h5，亲测可用。
没分的可以去我的github看看，下面有具体方法以及百度网盘下载地址，网址为：https://github.com/freenowill/Object-Detection
2021-08-31 15:46:03
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Programming Exercise 1: Linear Regression
Machine Learning
Introduction
In this exercise, you will implement linear regression and get to see it work
on data. Before starting on this programming exercise, we strongly recom-
mend watching the video lectures and completing the review questions for
the associated topics.
To get started with the exercise, you will need to download the starter
code and unzip its contents to the directory where you wish to complete the
exercise. If needed, use the cd command in Octave/MATLAB to change to
this directory before starting this exercise.
You can also find instructions for installing Octave/MATLAB in the “En-
vironment Setup Instructions” of the course website.
Files included in this exercise
ex1.m - Octave/MATLAB script that steps you through the exercise
ex1 multi.m - Octave/MATLAB script for the later parts of the exercise
ex1data1.txt - Dataset for linear regression with one variable
ex1data2.txt - Dataset for linear regression with multiple variables
submit.m - Submission script that sends your solutions to our servers
[?] warmUpExercise.m - Simple example function in Octave/MATLAB
[?] plotData.m - Function to display the dataset
[?] computeCost.m - Function to compute the cost of linear regression
[?] gradientDescent.m - Function to run gradient descent
[†] computeCostMulti.m - Cost function for multiple variables
[†] gradientDescentMulti.m - Gradient descent for multiple variables
[†] featureNormalize.m - Function to normalize features
[†] normalEqn.m - Function to compute the normal equations
? indicates files you will need to complete
† indicates optional exercises
2021-08-27 05:11:18
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