吴恩达机器学习第十一周测验

本文解析了关于PhotoOCR系统的测验题目,包括滑动窗口检测器的应用、数据标签成本估算、天花板分析的好处、获取更多正例的方法以及PhotoOCR系统的流程优化策略。

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测验:Application: Photo OCR

第一题

Suppose you are running a sliding window detector to find

text in images. Your input images are 1000x1000 pixels. You

will run your sliding windows detector at two scales, 10x10

and 20x20 (i.e., you will run your classifier on lots of 10x10

patches to decide if they contain text or not; and also on

lots of 20x20 patches), and you will “step” your detector by 2

pixels each time. About how many times will you end up

running your classifier on a single 1000x1000 test set image?
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答案
A
分析:每一次移动2个像素,故一轮循环需要移动将近500 * 500次,故两轮循环需要移动500,000次。

第二题

Suppose that you just joined a product team that has been

developing a machine learning application, using m = 1,000m=1,000

training examples. You discover that you have the option of

hiring additional personnel to help collect and label data.

You estimate that you would have to pay each of the labellers

$10 per hour, and that each labeller can label 4 examples per

minute. About how much will it cost to hire labellers to

label 10,000 new training examples?

在这里插入图片描述

答案
D

第三题

What are the benefits of performing a ceiling analysis? Check all that apply.
在这里插入图片描述
答案
CD

第四题

Suppose you are building an object classifier, that takes as input an image, and recognizes that image as either containing a car (y=1y=1) or not (y=0y=0). For example, here are a positive example and a negative example:
在这里插入图片描述
After carefully analyzing the performance of your algorithm, you conclude that you need more positive (y=1y=1) training examples. Which of the following might be a good way to get additional positive examples?
在这里插入图片描述
答案
A

第五题

Suppose you have a PhotoOCR system, where you have the following pipeline:
在这里插入图片描述
在这里插入图片描述

答案
AB

吴恩达机器学习ex2是指吴恩达在其机器学习课程中提供的第二个编程作业,即逻辑回归(Logistic Regression)的实现。这个实现是基于Matlab/Octave完成的。在这个作业中,学生需要根据给定的数据集实现逻辑回归算法,并进行模型训练和预测。 参考黄海广的笔记中的代码示例展示了一种使用Python实现的方法。首先,导入必要的库,包括numpy、pandas、matplotlib和scipy.optimize。然后,根据数据集的特点,初始化变量。代码中cols变量表示数据集的列数,X2表示除了第一列外的所有列的数据,y2表示第一列的数据。接下来,将X2和y2转换为数组类型,并创建一个长度为11的零数组theta2。最后,设定正则化参数为1,计算代价和梯度。 关于具体算法实现的细节,包括代价函数(costreg)和梯度函数(gradientReg),可以参考实际代码。<span class="em">1</span><span class="em">2</span><span class="em">3</span> #### 引用[.reference_title] - *1* [【机器学习吴恩达机器学习作业 ex2逻辑回归 Matlab实现](https://blog.youkuaiyun.com/m0_52427832/article/details/125358227)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"] - *2* *3* [吴恩达机器学习》课后测试Ex2:逻辑回归(详细Python代码注解)](https://blog.youkuaiyun.com/qq_44577070/article/details/120644061)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"] [ .reference_list ]
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