stanford checker

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines

Exercise:sparse Coding Contents [Hide] 1Sparse Coding 1.1Dependencies 1.2Step 0:initialization 1.3Step 1:sample patches 1.4Step 2:implement and check S Parse coding cost functions 1.5Step 3:iterative optimization Sparse Coding

Reprint please indicate the sourcehttp://blog.csdn.net/pony_maggie/article/details/27706991Author: PonyBecause the content of lesson five is more, it is divided into two parts to write.Basic operation of a screen rotationControls whether the current

Reprint please indicate the sourcehttp://blog.csdn.net/pony_maggie/article/details/27845257Author: PonyFive code examplesThe above mentioned knowledge points are covered in this example. In addition, I'm just here to analyze some important code,

The number of parameters in the 1,oc is different, it can be two completely different methods. Such as-(void) Addcard: (card *) Card attop: (BOOL) attop; -(void) Addcard: (card *) card; A second method can be implemented-(void) Addcard: (Card *)

1. Variable type do not misuse the ID, if it is not carefully easy to throw an error in the execution of the program, because in the compilation phase the compiler simply detects that the variable object belongs to the type, especially when the type

Original: http://blog.csdn.net/abcjennifer/article/details/7797502This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization,

8. Support Vector machines (SVMs) Content 　　　　8. Support Vector machines (SVMs) 8.1 Optimization Objection 8.2 Large Margin Intuition 8.3 Mathematics Behind Large Margin classification 8.4 Kernels 8.5 Using a SVM

Model (how to simulate)---strategy (risk function)-algorithm (optimization method)First section:Basic concepts and classifications of machine learningSection II:Linear regression, least squaresBatch gradient descent (BGD) and random gradient descent

Original: http://blog.csdn.net/abcjennifer/article/details/7700772This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization,

1. Iterative Solution to linear fitting Batch Gradient Descent The gradient descent method moves the parameter a small distance along the gradient direction each time. There are two specific implementations: one is to

This course comes to the end. I got a statement of accomplishment in Chinese Valentine's Day which is also called double seventh festival. In the last video, using sor Andrew Ng said a few words which impressed me a lot: And before wrapping up,

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. Problem description and pipeline ( Problem description and pipeline ) OCRYesOptical Character RecognitionOptical character recognition. In the

Public Course address:Https://class.coursera.org/ml-003/class/index  INSTRUCTOR:Andrew Ng 1. Problem motivation ( Problem generation ) Let's take a look at an example. If we want to inspect the aircraft engine, we know that the performance of

Public Course address:Https://class.coursera.org/ml-003/class/index  INSTRUCTOR:Andrew Ng 1. Learning with large datasets ( Big Data Learning ) The importance of data volume has been mentioned in the previous lecture on machine learning

Public Course address:Https://class.coursera.org/ml-003/class/index  INSTRUCTOR:Andrew Ng 1. Cost Function ( Cost functions ) The last lecture introduced the multiclass classification problem. The difference between the multiclass

Public Course address:Https://class.coursera.org/ml-003/class/index  INSTRUCTOR:Andrew Ng 1. Prioritizing what to work on: Spam classification example ( Spam Classification System ) I have learned some theoretical knowledge and diagnostic

Public Course address:Https://class.coursera.org/ml-003/class/index  INSTRUCTOR:Andrew Ng 1. Classification ( Category ) Consider a system for predicting patient tumors that can determine whether a patient's tumor is benign or malignant. We

Generative Learning and discriminant learningLike logistic regression, hθ (x) = g (ΘTX) is used to model P (y|x;θ) directly, or, like a perceptron, directly from the input space to the output space (0 or 1), they are called discriminant Learning

Anomaly Detectionproblem Motivation:First example of anomaly detection: aircraft engine anomaly detectionIntuitively it is found that if the new engine is in the middle, we may think that it is OK, if the deviation is very large, we need more