Machine learning 吴恩达第二周coding作业(必做题）

【Machine learning 吴恩达第二周coding作业(必做题）】的更多相关文章

Machine learning 吴恩达第二周coding作业(必做题）

1.warmUpExercise: function A = warmUpExercise() %WARMUPEXERCISE Example function in octave % A = WARMUPEXERCISE() is an example function that returns the 5x5 identity matrix A = []; % ============= YOUR CODE HERE ============== % Instructions: Return…

Machine learning吴恩达第二周coding作业（选做）

1.Feature Normalization: 归一化的处理 function [X_norm, mu, sigma] = featureNormalize(X) %FEATURENORMALIZE Normalizes the features in X % FEATURENORMALIZE(X) returns a normalized version of X where % the mean value of each feature is 0 and the standard dev…

Machine Learning——吴恩达机器学习笔记（酷

[1] ML Introduction a. supervised learning & unsupervised learning 监督学习:从给定的训练数据集中学习出一个函数(模型参数),当新的数据到来时,可以根据这个函数预测结果.监督学习的训练集要求包括输入输出,也可以说是特征和目标.训练集中的目标是由人标注的.常用于:训练神经网络.决策树.回归分析.统计分类无监督学习:输入数据没有被标记,也没有确定的结果.样本数据类别未知,需要根据样本间的相似性对样本集进行分类,试图使类内差距最小化,…

Machine learning吴恩达第三周 Logistic Regression

1. Sigmoid function function g = sigmoid(z) %SIGMOID Compute sigmoid function % g = SIGMOID(z) computes the sigmoid of z. % You need to return the following variables correctly g = zeros(size(z)); % ====================== YOUR CODE HERE =============…