PrefaceThe Machine learning section records Some of the notes I have learned in the process of learning, including the online course or tutorial's study notes, the reading notes of the papers, the debugging of algorithmic code, the thinking of cutting-edge theory and so on, which will open different column series for different content.Machine learning is an excit

! I've been on this course 3 years ago, and it's been a long time ... Before going to bed to see this question, the day before yesterday wrote an article about learning Python in Coursera, just right question, so excerpt part, hope to be helpful:-)
Let's talk about the process of learning Python in Coursera (and reco

friends leave a message saying they are already charged. Let's go to the official website and check it out! I have taken this course three years ago. It takes a long time ...... I saw this problem before I went to bed. I wrote an article about learning python in coursera the day before yesterday, which is just the right question. So I want to extract some of it and hope it will help me :-)
Next, let's ta

:
For logistic regression, it is similar to linear regression:-First, the training set learns the parameter θ;-Calculate the test set error:
-Add an error (or 0/1 error) for the error category );
3) Model selection and training/validation/test sets (Model selection and training/verification/test Set)
First, let's review the above overfitting example:
Once the θ 0, θ 1 ,..., θ 4 for certain datasets (training sets) Adaptation (final learning paramet

WEEK1:Machine learning:
A computer program was said to learn from experience E with respect to some class of tasks T and performance measure P, if Its performance on tasks in T, as measured by P, improves with experience E.
Supervised learning:we already know what we correct output should look like.
Regression:try to map input variables to some continuous function.

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 consider each English word, or easy to make mist

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 said to l

Programmers who have turned to AI have followed this number ☝☝☝
Author: Lisa Song
Microsoft Headquarters Cloud Intelligence Advanced data scientist, now lives in Seattle. With years of experience in machine learning and deep learning, we are familiar with the requirements analysis, architecture design, algorithmic development and integrated deployment of machine lear

This series is a personal learning note for Andrew Ng Machine Learning course for Coursera website (for reference only)Course URL: https://www.coursera.org/learn/machine-learning Exercise 7--k-means and PCA
Download coursera-Wunda

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 and online courses such as UFLDL Tuto

First, how to learn a large-scale data set?In the case of a large training sample set, we can take a small sample to learn the model, such as m=1000, and then draw the corresponding learning curve. If the model is found to be of high deviation according to the learning curve, the model should continue to be adjusted on

Before the machine learning is very interested in the holiday cannot to see Coursera machine learning all the courses, collated notes in order to experience repeatedly.I. Introduction (Week 1)-What's machine learningThere is no unanimous answer to the definition of machine learning.Arthur Samuel (1959) gives a definition of machine learning:Machine

9 Anomaly Detection9.1 Density Estimation9.1.1 Problem MotivationAnomaly detection (Density estimation) is a common application of machine learning and is mainly used for unsupervised learning, but in some ways it is similar to supervised learning.The most common application of anomaly detection is fraud detection and in the industrial production field.In particular, the industrial production of aircraft en

points of mini project are translated, Then translate the Mini project implementation steps, not a one-time full translation, take too long, the previous translation may forget, and the translation may not be accurate, and sometimes to see the original text. Complete a paragraph and translate the next paragraph, step by step. Do not translate all, some do not help to complete the task can not translate, save time. 4. Selective translation of code clinic,5. If you get stuck, search for keywords

Https://www.coursera.org/learn/machine-learning/exam/dbM1J/octave-matlab-tutorial
Octave Tutorial
5 questions
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 is then valid Octave commands? Check all, apply and assume all options is written in an Octave command. (HINT:A ' denotes the transpose of A.)

use of MATLAB. *.4.gradientdescent.mfunction [Theta, j_history] =gradientdescent (X, y, theta, Alpha, num_iters)%gradientdescent performs gradient descent to learn theta% theta = gradientdescent (X, y, theta, Alpha, num_iters) up Dates theta by% taking num_iters gradient steps with learning rate alpha% Initialize Some useful valuesm= Length (y);%Number of training examplesj_history= Zeros (Num_iters,1); f

minimum functionRegular equation method gradient descent can be better extended to large datasets for a large number of contexts and machine learning next-important extensions
The regular equation of extended numerical solution of two algorithms in order to solve the minimization problem of [min J (θ0,θ1)], we use the exact numerical method rather than the constant iterative gradient descent method with the advantages and disadvantages of the regular

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 introducedThe composition of the threadImplementatio

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