mit machine learning course online

July Algorithm-December machine Learning online Class -12th lesson note-Support vector machine (SVM) July algorithm (julyedu.com) December machine Learning Online class study note http:

Tags: deviation chinese data cts You multitasking performance GPO ESCLearning Goals Understand what multi-task learning and transfer learning is Recognize bias, variance and data-mismatch by looking in the performances of your algorithm on train/dev/test sets "Chinese Translation"Learning GoalsLearn what multi-tasking

these books may not be appropriate for beginners.Further Reading Continue readingIn writing this article, I carefully considered the relevant issues, but also refer to other people recommended information to ensure that I did not omit any important reference. In order to ensure the integrity of the article, the following is also listed in some popular online, available for beginners to use the material. A list of Data science and

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 classification problem and the binary classification problem lies in that there are multiple output units, which are summarized as follows: At the same time, we also know the price functions of Logistic regress

-image-url ' ... Case ' Course-effort ': This.setfield (event); break;+ //added by wwj+ case ' course-category ': + This.setfield (event); + Break ;Vim Cms/static/js/models/settings/course_details.js Effort:null, //an int or null,+ category:null,HtmlVim cms/templates/settings.html+ # #added by wwj+% if about_page_edi

Finally the end of the final, look at others summary: http://blog.sina.com.cn/s/blog_641289eb0101dynu.htmlContact Machine Learning also has a few years, but still only a rookie, when the first contact English is not good, do not understand the class, what things are smattering. After learning some open classes and books on the go, I began to understand some conce

Tags: tutorial set Test skills Virtualization ATI Introduction Operations Services1th Stage Basic Course -01 vmwareworkstation Virtual machine Use tutorialSuitable for objectsLearning systems and network IT courses require you to be able to build enterprise networks and server learning and experimentation environments on physical machines, and the skilled use of

calculates the accuracy of the entire system at this time: As shown in, text recognition consists of four parts. Now we can find the system accuracy after optimization for each part. The question is, how can we improve the accuracy of the entire system? We can see from the table that, if we have optimized the text moderation part, the accuracy will be72%Add89%If we optimize the character segmentation, the accuracy is only from89%To90%If character recognition is optimized90%To100%In contr

learning.In fact, these two states are not completely divided, for example, if we are trading in a lot of fraud, then we study the problem from anomaly detection to supervise learning.Exercise: Intuitive judgment of two situationsChoosingwhat Features to useThe previous approach is to assume that the data satisfies the Gaussian distribution, and also mentions that if the distribution is not Gaussian distribution, the above method can be used, but if we convert the distribution to approximate Ga

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 current plan is to see the end of the neural netw

Tags: machine learning, data mining, overfitting, deterministic noiseCourse introductionThis section describes the problem of over-generalization in machine learning. The author points out that one of the ways to differentiate a professional-level player from a hobbyist is how they deal with the problem of preparation.

is close to the global minimum. In fact, you can dynamically adjust the learning rate α= constant 1/(number of iterations + constant 2), so that as the iteration, α gradually reduced, in favor of the final convergence to the global minimum value. However, because "constant 1" and "Constant 2" is not OK, so often set α is fixed.How do you judge the convergence of the model as the iteration progresses? Every 1000 or 5,000 samples, the J value of these

represent the right side of the inequality and Delta to represent ε. So we have: We have previously studied the probability of occurrence of bad events. Now let's look at the probability of occurrence of optimistic events: P [| ein (G)-eout (G) | Use Ω (n, H, Delta) instead of ε to get the desired good event definition: | eout-Ein | Ω is positively related to N, Delta, and h or VC. We ignore the Ω parameter first, so there are: | eout-Ein | In most cases, eout is larger than EIN, because w

does not introduce a matrix, which is easy to calculate and can be correctly executed if there are few samples. The multi-element model is complex to calculate after the matrix is introduced. to calculate the inverse of the matrix, the model must be executed when the sample value is greater than the feature value. ------------------------------------------Weak split line---------------------------------------------- Although exception detection is mentioned in this article, it is used to in

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 Exerci

+ 1 parameter: x0 -- x256. We hope to use machine learning to determine the values of all these parameters. However, with so many parameters, machine learning may take a lot of time to complete, and the effect is not necessarily good. We can see that some pixels are not needed, so we should extract some features from

(that is, Xi in {1,..., | v|} Value in | V| is the vocabulary of the lexicon), n-word messages will be represented by a vector of length n, and the length of the vectors for different articles will probably not be the same.In the multiple event model, we assume that this is the case with the message: first determine whether this is a spam message through P (Y), and then independently determine each word by multiple distributions P (x|y). The probability of the final generation of the entire mes

It should be this time last year, I started to get into the knowledge of machine learning, then the introductory book is "Introduction to data mining." Swallowed read the various well-known classifiers: Decision Tree, naive Bayesian, SVM, neural network, random forest and so on; In addition, more serious review of statistics, learning the linear regression, but a

Andrew ng Machine Learning course 17 (2)Disclaimer: Reference Please specify source http://blog.csdn.net/lg1259156776/Description: This paper mainly introduces the use of value iteration and policy iteration two kinds of iterative algorithms to solve MDP problem, also introduced in practical application how to accumulate "experience" to update the transfer probab

July algorithm December machine learning online Class---20th lesson notes---deep learning--rnnJuly algorithm (julyedu.com) December machine Learning Online class study note http://www.j