1.Gaussian Kernel function sim = gaussianKernel(x1, x2, sigma) %RBFKERNEL returns a radial basis function kernel between x1 and x2 % sim = gaussianKernel(x1, x2) returns a gaussian kernel between x1 and x2 % and returns the value in sim % Ensure that…

1.linearRegCostFunction: function [J, grad] = linearRegCostFunction(X, y, theta, lambda) %LINEARREGCOSTFUNCTION Compute cost and gradient for regularized linear %regression with multiple variables % [J, grad] = LINEARREGCOSTFUNCTION(X, y, theta, lamb…

1.findClosestCentroids function idx = findClosestCentroids(X, centroids) %FINDCLOSESTCENTROIDS computes the centroid memberships for every example % idx = FINDCLOSESTCENTROIDS (X, centroids) returns the closest centroids % in idx for a dataset X wher…

1.Sigmoid Gradient function g = sigmoidGradient(z) %SIGMOIDGRADIENT returns the gradient of the sigmoid function %evaluated at z % g = SIGMOIDGRADIENT(z) computes the gradient of the sigmoid function % evaluated at z. This should work regardless if z…

1.lrCostFunction: 和第三周的那个一样的: function [J, grad] = lrCostFunction(theta, X, y, lambda) %LRCOSTFUNCTION Compute cost and gradient for logistic regression with %regularization % J = LRCOSTFUNCTION(theta, X, y, lambda) computes the cost of using % theta…

问题描述: 做吴恩达深度学习第4课第3周编程作业时导入PIL包报错． 我的环境: 已经安装了Tensorflow GPU 版本 Python3 Anaconda 解决办法: 安装pillow模块,而不是PIL模块．原因是PIL模块不支持python3, 只支持python2. Python3下应安装pillow模块代, 替PIL即可． 下面内容是根据自己环境的记录: Ubuntu下我用Anaconda已经搭建好了Tensorflow GPU环境,环境名字是tf. 命令行下输入(下面命令不加sud…

我python2.7, 做吴恩达深度学习第2课第2周编程作业 Optimization Methods 时有2个坑: 第一坑 需将辅助文件 opt_utils.py 的 nitialize_parameters(layer_dims) 函数中的 2 改成 2.0 , 保存后再重启jupyter notebook. 第二坑 需将辅助文件 opt_utils.py 的 plot_decision_boundary(model, X, y) 函数中的 c=y 改成 c=y[0], 保存后再重启jupy…

做题记录 风影影,景色明明,淡淡云雾中,小鸟轻灵. c++的文件操作已经好玩起来了,不过掌握好控制结构显得更为重要了. 我这也不做啥题目分析了,直接就题干-代码. 总结--留着自己看 1. 流是指从一个位置向另一个位置传输的一连串数据的集合. 2. 标准输入流一一从标准输入设备(键盘)流向程序的数据.,一般用cin流对象进行输入 3. ">>",这个符号是输入ios类的符号重载函数.以回车和空格作为分隔符.就是说中间的空格符号无法获得. 4. 着重介绍:get 和 getl…

SVMs are considered by many to be the most powerful 'black box' learning algorithm, and by posing构建 a cleverly-chosen optimization objective优化目标, one of the most widely used learning algorithms today. 第一节 向量的内积(SVM的基本数学知识) Support Vector Machines 支持向…

In Week 6, you will be learning about systematically improving your learning algorithm. The videos for this week will teach you how to tell when a learning algorithm is doing poorly, and describe the 'best practices' for how to 'debug' your learning…