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Coursera open course notes: "Advice for applying machine learning", 10 class of machine learning at Stanford University )"

networks and overfitting: The following is a "small" Neural Network (which has few parameters and is easy to be unfitted ): It has a low computing cost. The following is a "big" Neural Network (which has many parameters and is easy to overfit ): It has a high computing cost. For the problem of Neural Network overfitting, it can be solved through the regularization (λ) method. References: Machine Learning

coursera-Wunda-Machine learning-(programming exercise 7) K mean and PCA (corresponds to the 8th week course)

This series is a personal learning note for Andrew Ng Machine Learning course for Coursera website (for reference only)Course URL:

Coursera Course "Machine learning" study notes (WEEK1)

This is a machine learning course that coursera on fire, and the instructor is Andrew Ng. In the process of looking at the neural network, I did find that I had a problem with a weak foundation and some basic concepts, so I wanted to take this course to find a leak. The curr

Coursera Machine Learning Techniques Course Note 01-linear Hard SVM

Extremely light of a semester finally passed, summer vacation intends to learn the big step down this machine learning techniques.The first lesson is the introduction of SVM, although I have learned it before, but I heard a feeling is very rewarding. The blogger sums up a ballpark figure, and the specifics areTo listen: blogger sums it up in detail: http://w

Coursera Machine Learning Course note-Hazard of Overfitting

dimension.Finally, we propose a method for solving overfitting, including data cleaning/pruning, data hinting, regularization (regularization), confirmation (validation), andTo drive for example to illustrate the role of these methods, the latter two methods are also the contents of the following two lessons.Data cleaning/pruning is to correct or delete the wrong sample points, processing is simple, but usually such sample points are not easy to find.Data hinting generate more sample numbers by

Coursera Machine Learning Course note--regularization

This section is about regularization, in the optimization of the use of regularization, in class when the teacher a word, not too much explanation. After listening to this class,To understand the difference between a good university and a pheasant university. In short, this is a very rewarding lesson.First of all, we introduce the reason for regularization, simply say that the complex model with a simple model to express, as to how to say, there is a series of deduction hypothesis, very creative

Coursera Big Machine Learning Course note 8--Linear Regression for Binary classification

I've been talking about why machines can learn, and starting with this lesson are some basic machine learning algorithms, i.e. how machines learn.This lesson is about linear regression, starting with the minimization of Ein, introducing the Hat Matrix to understand the geometric meaning. Finally, the linear regression and binary classification are compared, and the reason why linear regression can be used t

Coursera Machine Learning Techniques Course Note 09-decision Tree

This is what we have learned (except decision tree)Here is a typical decision tree algorithm, with four places to choose from:Then introduced a cart algorithm: By decision Stump divided into two categories, the criterion for measuring subtree is that the data are divided into two categories, the purity of these two types of data (purifying).The following is a measure of purity:Finally, when to stop:Decision tree may be overfitting, reducing the number of Ein and leaves (indicating the complexity

Coursera Machine Learning Course note--Linear Models for classification

In this section, a linear model is introduced, and several linear models are compared, and the linear regression and the logistic regression are used for classification by the conversion error function.More important is this diagram, which explains why you can use linear regression or a logistic regression to replace linear classificationThen the stochastic gradient descent method is introduced, which is an improvement to the gradient descent method, which greatly improves the efficiency.Finally

Coursera Machine Learning Techniques Course Note 03-kernel Support Vector machines

This section is about the nuclear svm,andrew Ng's handout, which is also well-spoken.The first is kernel trick, which uses nuclear techniques to simplify the calculation of low-dimensional features by mapping high-dimensional features. The handout also speaks of the determination of the kernel function, that is, what function K can use kernel trick.In addition, the kernel function can measure the similarity of two features, the greater the value, the more similar.Next is the polynomial Kernel, w

Machine Learning| Andrew ng| Coursera Wunda Machine Learning Notes

continuously updating theta. Map Reduce and Data Parallelism: Many learning algorithms can be expressed as computing sums of functions over the training set. We can divide up batch gradient descent and dispatch the cost function for a subset of the data to many different machines So, we can train our algorithm in parallel. Week 11:Photo OCR: Pipeline: Text detection Character segmentation Ch

[Machine Learning] Coursera notes-Support Vector machines

friends, but also hope to get the high people of God's criticism!        Preface  [Machine Learning] The Coursera Note series was compiled with notes from the course I studied at the Coursera learning (Andrew ng teacher). The con

Note for Coursera "Machine learning" 1 (1) | What are machine learning?

What are machine learning?The definitions of machine learning is offered. Arthur Samuel described it as: "The field of study that gives computers the ability to learn without being explicitly prog Rammed. " This was an older, informal definition.Tom Mitchell provides a more modern definition: 'a computer program was sa

Operating system Learning notes----process/threading Model----Coursera Course notes

Operating system Learning notes----process/threading Model----Coursera Course note process/threading model 0. Overview 0.1 Process ModelMulti-Channel program designConcept of process, Process control blockProcess status and transitions, process queuesProcess Control----process creation, revocation, blocking, wake-up 、...0.2 threading ModelWhy threading is introdu

"MATLAB" machine learning (Coursera Courses Outline & Schedule)

The course covers technology:Gradient descent, linear regression, supervised/unsupervised learning, classification/logistic regression, regularization, neural network, gradient test/numerical calculation, model selection/diagnosis, learning curve, evaluation metric, SVM, K-means clustering, PCA, Map Reduce Data Parallelism, etc...The

Coursera "Machine learning" Wunda-week1-03 gradient Descent algorithm _ machine learning

Gradient descent algorithm minimization of cost function J gradient descent Using the whole machine learning minimization first look at the General J () function problem We have J (θ0,θ1) we want to get min J (θ0,θ1) gradient drop for more general functions J (Θ0,θ1,θ2 .....) θn) min J (θ0,θ1,θ2 .....) Θn) How this algorithm works. : Starting from the initial assumption Starting from 0, 0 (or any other valu

Machine Learning Coursera Learning Summary

Coursera Andrew Ng Machine learning is really too hot, recently had time to spend 20 days (3 hours a day or so) finally finished learning all the courses, summarized as follows:(1) Suitable for getting started, speaking the comparative basis, Andrew speaks great;(2) The exercise is relatively easy, but to carefully con

Python detailed process of crawling Coursera course resources, coursera Course Resources

assigned to others, then the median is the score of each job. UW's two courses are videos of UW's class directly, and the homework of machine correction is boring. Therefore, Coursera's courses are also uneven and need to be screened, but the overall quality is still relatively high. I plan to take some social science courses now. I am waiting for the course class to begin with an English writing

Neural Network jobs: NN Learning Coursera machine learning (Andrew Ng) WEEK 5

)/m; at End - End - -%size (J,1) -%size (J,2) - ind3 = A3-Ty; -D2 = (D3 * THETA2 (:,2: End)). *sigmoidgradient (z2); toTheta1_grad = Theta1_grad + d2'*a1/m; +Theta2_grad = Theta2_grad + d3'*a2/m; - the% ------------------------------------------------------------- *jj=0; $ Panax Notoginseng forI=1: Size (Theta1,1) - forj=2: Size (Theta1,2) theJJ = JJ + Theta1 (i,j) *theta1 (i,j) *lambda/(m*2); + End A End theSize (Theta1,1); +Size (Theta1,2); - $ forI=1: Size (THETA2,1) $

[Machine Learning] Coursera ml notes-Logistic regression (logistic Regression)

IntroductionThe Machine learning section records Some of the notes I've learned about the learning process, including linear regression, logistic regression, Softmax regression, neural networks, and SVM, and the main learning data from Standford Andrew Ms Ng's tutorials in Coursera

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