[ML] Gradient Descend Algorithm [Octave code]

function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)
m = length(y); % number of training examples
J_history = zeros(num_iters, 1);

for iter = 1:num_iters
    theta = theta - alpha * X‘ * (X * theta - y) / m;
    iter = iter +1;
   J_history(iter) = computeCostMulti(X, y, theta);

end

end

  

时间: 2024-10-10 00:56:15

[ML] Gradient Descend Algorithm [Octave code]的相关文章

(转)Introduction to Gradient Descent Algorithm (along with variants) in Machine Learning

Introduction Optimization is always the ultimate goal whether you are dealing with a real life problem or building a software product. I, as a computer science student, always fiddled with optimizing my code to the extent that I could brag about its

A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning

A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning by Jason Brownlee on September 9, 2016 in XGBoost 0 0 0 0 Gradient boosting is one of the most powerful techniques for building predictive models. In this post you will dis

How to Configure the Gradient Boosting Algorithm

How to Configure the Gradient Boosting Algorithm by Jason Brownlee on September 12, 2016 in XGBoost 0 0 0 0 Gradient boosting is one of the most powerful techniques for applied machine learning and as such is quickly becoming one of the most popular.

6 Easy Steps to Learn Naive Bayes Algorithm (with code in Python)

6 Easy Steps to Learn Naive Bayes Algorithm (with code in Python) Introduction Here’s a situation you’ve got into: You are working on a classification problem and you have generated your set of hypothesis, created features and discussed the importanc

CEPH CRUSH 算法源码分析 原文CEPH CRUSH algorithm source code analysis

原文地址 CEPH CRUSH algorithm source code analysis http://www.shalandis.com/original/2016/05/19/CEPH-CRUSH-algorithm-source-code-analysis/ 文章比较深入的写了CRUSH算法的原理和过程.通过调试深入的介绍了CRUSH计算的过程.文章中添加了些内容. 写在前面 读本文前,你需要对ceph的基本操作,pool和CRUSH map非常熟悉.并且较深入的读过源码. 分析的方法

[ML] CostFunction [Octave code]

function J = computeCostMulti(X, y, theta) m = length(y); % number of training examples J = 0; for i = 1:m J = J + (X(i,:) * theta - y(i,1)) ^ 2 end; J = J / (2 * m); end

[工具]Loop subdivision algorithm+matlab code

引言:分析Loop subdivision algorithm的原理及代码实现.该算法可以用在对3D网格的细分上.同时,在形状检索领域,经常需要选取视点(viewpoint)来对模型进行绘制(render),例如,在zhouhui lian的CM-BOF方法中,对正八面体进行细化分解得到均匀的视点分布. 关于原理,有一些中文博客,但是讲的不是太详细,对照代码可能可以看的更清楚. 直接上代码吧.中文注释是我注释的,代码是在mathwork上看到的. function [newVertices, n

(转)Let’s make a DQN 系列

Let's make a DQN 系列 Let's make a DQN: Theory September 27, 2016DQN This article is part of series Let's make a DQN. 1. Theory2. Implementation3. Debugging4. Full DQN5. Double DQN and Prioritized experience replay (available soon) Introduction In Febr

BP反向传播算法的工作原理How the backpropagation algorithm works

In the last chapter we saw how neural networks can learn their weights and biases using the gradient descent algorithm. There was, however, a gap in our explanation: we didn't discuss how to compute the gradient of the cost function. That's quite a g