1. 机器学习是什么? "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."     -Tom Mitchell 2.…

CS229 Machine Learning Stanford Course by Andrew Ng Course material, problem set Matlab code written by me, my notes about video course: https://github.com/Yao-Yao/CS229-Machine-Learning Contents: supervised learning Lecture 1 application field, pre-…

Practical Machine Learning For The Uninitiated Last fall when I took on ShippingEasy's machine learning problem, I had no practical experience in the field. Getting such a task put on my plate was somewhat terrifying, and even more so as we started t…

https://www.quora.com/How-do-I-learn-machine-learning-1?redirected_qid=6578644   How Can I Learn X? Learning Machine Learning Learning About Computer Science Educational Resources Advice Artificial Intelligence How-to Question Learning New Things Lea…

Machine Learning by Andrew Ng | Stanford University | Coursera https://www.coursera.org/learn/machine-learning Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has g…

7 Machine Learning System Design Content 7 Machine Learning System Design 7.1 Prioritizing What to Work On 7.2 Error Analysis 7.3 Error Metrics for Skewed Classed 7.3.1 Precision/Recall 7.3.2 Trading off precision and recall: F1 Score 7.4 Data for ma…

源码:https://github.com/cheesezhe/Coursera-Machine-Learning-Exercise/tree/master/ex5 Introduction: In this exercise, you will implement regularized linear regression and use it to study models with different bias-variance properties. 1. Regularized Lin…

1. Sigmoid Function In Logisttic Regression, the hypothesis is defined as: where function g is the sigmoid function. The sigmoid function is defined as: 2.Cost function and gradient The cost function in logistic regression is: the gradient of the cos…

(1) How to comput the Cost function in Univirate/Multivariate Linear Regression; (2) How to comput the Batch Gradient Descent function in Univirate/Multivariate Linear Regression; (3) How to scale features by mean value and standard deviation; (4) Ho…

本栏目来源于Andrew NG老师讲解的Machine Learning课程,主要介绍大规模机器学习以及其应用.包括随机梯度下降法.维批量梯度下降法.梯度下降法的收敛.在线学习.map reduce以及应用实例:photo OCR.课程地址为:https://www.coursera.org/course/ml (一)大规模机器学习 从前面的课程我们知道,如果我们的系统是high variance的,那么增加样本数会改善我们的系统,假设现在我们有100万个训练样本,可想而知,如果使用梯度下降法,…