wangcong02345的专栏

Week3_1Logistic Regression第 1 题Suppose that you have trained a logistic regression classifier, and it outputs on a new example x a prediction hθ(x)h_\theta(x) = 0.4. This means (check all that a

Week6_1Advice for Applying Machine LearningWeek6_1Advice for Applying Machine Learning第 1 题第 2 题第 3 题第 4 题第 5 题第 1 题You train a learning algorithm, and find that it has unaccepta...

w6_2Regularized Linear Regression and BiasVariance编程解析1. ex5 流程分析1.1 plotData按照ex5.m中的流程走一遍2. 编程分析2.0 计算公式代价函数： hθ(x)=12m∑i=1m(hθ(x(i)−y(i))2+λ2m(∑j=1nθ2j)hθ(x)=12m∑i=1m(hθ(...

Week6_3Machine Learning System Design第 1 题You are working on a spam classification system using regularized logistic regression. “Spam” is a positive class (y = 1) and “not spam” is the negative...

Week7_1Support Vector Machinesg第 1 题Suppose you have trained an SVM classifier with a Gaussian kernel, and it learned the following decision boundary on the training set: You suspect that the...

Week7_2Program_Support Vector Machines编程解析1. ex6 流程分析1.1 plotData按照ex6.m中的流程走一遍2. 编程分析2.0 计算公式hθ(x)=minθC∑i=1m[y(i)cost1(θTx(i))+(1−y(i))cost0(θTx(i))]+12∑i=1nθ2jhθ(x)=minθC∑...

Week8_1Unsupervised LearningWeek8_1Unsupervised Learning第 1 题第 2 题第 3 题第 4 题第 5 题第 1 题For which of the following tasks might K-means clustering be a suitable algorithm? Select al...

Week8_2Principal Component Analysis第 1 题Consider the following 2D dataset: Which of the following figures correspond to possible values that PCA may return for u(1)u(1)u^{(1)} (the first eigen...

Week8_3Program_K-Means Clustering and PCA编程解析1. ex7 流程分析1.1 plotData按照ex7.m中的流程走一遍2. 编程分析2.1 Find closest centroids 0 / 30计算公式: ||x(i)−μj||2||x(i)−μj||2||x^{(i)}- \mu_j||^2 ...

Week9_1Anomaly DetectionWeek9_1Anomaly Detection第 1 题第 2 题第 3 题第 4 题第 5 题第 1 题For which of the following problems would anomaly detection be a suitable algorithm?Given an im...

Week9_2Recommender SystemsWeek9_2Recommender Systems第 1 题第 2 题第 3 题第 4 题第 5 题第 1 题Suppose you run a bookstore, and have ratings (1 to 5 stars) of books. Your collaborative filt...

Week9_3Program_Anomaly Detection and Recommender Systems编程解析1. Anomaly detection分析1.1 Estimate gaussian parameters 0 / 15计算公式: μi=1m∑j=1mx(j)iμi=1m∑j=1mxi(j)\mu_i = \frac{1}{m}\sum\limits_...

Week10_1Large Scale Machine LearningWeek10_1Large Scale Machine Learning第 1 题第 2 题第 3 题第 4 题第 5 题第 1 题Suppose you are training a logistic regression classifier using stochastic gr...

w5_2Neural Network Learning编程解析1. ex4 流程分析1.1 plotData按照ex4.m中的流程走一遍%% Setup the parameters you will use for this exerciseinput_layer_size = 400; % 20x20 Input Images of Digitshidde...

Week5_1Neural Networks LearningWeek5_1Neural Networks Learning第 1 题第 2 题第 3 题第 4 题第 5 题第 1 题You are training a three layer neural network and would like to use backpropagation t...

w4_2Multi-class Classification and Neural Networks编程解析w4_2Multi-class Classification and Neural Networks编程解析ex3m1 ex3m框架分析2 Regularized logistic regression 0 3021 logistic regression Cost ...

Week 1 | 1_Introduction第 1 题A computer program is said to learn from experience E with respect to some task T and some performance measure P if its performance on T, as measured by P, improves

Week 1 | 3_Linear Algebra第 1 题Let two matrices be A=[4639],B=[−2−592]A = \begin{bmatrix} 4 & 3 \\ 6 & 9 \end{bmatrix}, \;\;\;\;\;\;\;\;\;\; B = \begin{bmatrix} -2 & 9 \\ -5 & 2 \end{bmatrix

Week 1 | 1_Linear Regression with One Variable第 1 题Consider the problem of predicting how well a student does in her second year of college/university, given how well she did in her first year.

Week 2 | 1Linear Regression with Multiple Variables第 1 题Suppose m=4 students have taken some class, and the class had a midterm exam and a final exam. You have collected a dataset of their score

Week 2 | 2_OctaveMatlab Tutorial第 1 题Suppose I first execute the following in Octave/Matlab: A = [1 2; 3 4; 5 6]; B = [1 2 3; 4 5 6]; Which of the following are then valid commands? Check al

Week 0 FAQ一. 各种问题1.1 win7无法播放视频的问题a. 修改host文件, 加入以下内容52.84.246.90 d3c33hcgiwev3.cloudfront.net52.84.246.252 d3c33hcgiwev3.cloudfront.net52.84.246.144 d3c33hcgiwev3.cloudfront.n

Week3_2Regularization第 1 题You are training a classification model with logistic regression. Which of the following statements are true? Check all that apply.Introducing regularization to t

w3_3Program_Logistic Regression编程解析1. Logistic Regression1.1 plotData把数据可视化, 在plotData.m中添加:% Find Indices of Positive and Negative Examplespos = find(y==1); neg = find(y == 0);% Plot

Week 2 | octive的安装及使用一. 源码安装octive环境: ubuntu 16.04 x64 最终的解决方法,可直接看 下方1.3 1.1 直接用apt-get 安装版本不对cong@msi:~$ sudo apt-get install octave cong@msi:~$ octave-cli GNU Octave, version

Week 2 | 3Program_Linear Regression编程解析1. 单变量1.1 Warm up exercise 10 / 10写出一个5阶单位矩阵 A = eye(5);1.2 Compute cost for one variable 40 / 40写出 损失函数 J(θ)=12m∑i=1m(hθ(x(i))−y(i))2J\le

Coursera机器学习笔记1-单变量线性回归Coursera机器学习笔记1-单变量线性回归一 理论1 训练集2 基本定义21 假设函数补充 关于假设函数的向量表示22 损失函数平方误差函数3 梯度下降算法4 推导过程5 可编程的theta更新过程二 核心代码说明111 逐行说明三 全部代码1 全部代码附录 参考文献...

Week4_1Neural Networks RepresentationWeek4_1Neural Networks Representation第 1 题第 2 题第 3 题第 4 题第 5 题第 1 题Which of the following statements are true? Check all that apply.The ac...

Week11_1Application Photo OCR第 1 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...