昨天总结了深度学习的资料,今天把机器学习的资料也总结一下(友情提示:有些网站需要"科学上网"^_^) 推荐几本好书: 1.Pattern Recognition and Machine Learning (by Hastie, Tibshirani, and Friedman's ) 2.Elements of Statistical Learning(by Bishop's) 这两本是英文的,但是非常全,第一本需要有一定的数学基础,第可以先看第二本.如果看英文觉得吃力,推荐看一下下面…
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…
Octave Tutorial 第一课 Computation&Operation 数据表示和存储 1.简单的四则运算,布尔运算,赋值运算(a && b,a || b,xor(a,b))等. 注意:(1)在Octave中,"不等于"的符号是"~=".(2)用%做注释.(3)变量后面接:抑制打印输出. 2.矩阵表示 (1)行矩阵(1行3列) [a1,a2,a3] (2)列矩阵(3行1列) [a1;a2;a3] (3)从1以步长为0.1到达2的(…
Machine Learning – Coursera Octave for Microsoft Windows GNU Octave官网 GNU Octave帮助文档 (有900页的pdf版本) Octave 4.0.0 安装 win7(文库) Octave学习笔记(文库) octave入门(文库) WIN7 64位系统安装JDK并配置环境变量(总是显示没有安装Java) MathWorks This week we're covering linear regression with mul…
https://www.coursera.org/learn/machine-learning/exam/dbM1J/octave-matlab-tutorial Octave Tutorial 5 试题 1. Suppose I first execute the following Octave commands: A = [1 2; 3 4; 5 6]; B = [1 2 3; 4 5 6]; Which of the following are then valid Octave com…
Frequently Asked Questions Congratulations to be part of the first class of the Deep Learning Specialization! This form is here to help you find the answers to the commonly asked questions. We will update it as we receive new questions that we think…
<Machine Learning>系列学习笔记 第一周 第一部分 Introduction The definition of machine learning (1)older, informal definition--Arthur Samuel--"the field of study that gives computers the ability to learn without being explicitly programmed." (2)modern d…
In this tutorial, we'll build a simple Universal Windows Platform application that uses a trained machine learning model to recognize a numeric digit drawn by the user. This tutorial primarily focuses on how to load and use Windows ML in your UWP app…
1.Introduction 1.1 Example        - Database mining        Large datasets from growth of automation/web.        E.g., Web click data, medical records, biology, engineering        - Applications can't program by hand.        E.g., Atonomous helicopter…
Learning Goals Understand why Machine Learning strategy is important Apply satisficing and optimizing metrics to set up your goal for ML projects Choose a correct train/dev/test split of your dataset Understand how to define human-level performance U…