machine learning course on coursera

classifier will be severely affected, as shown in:To solve the above two problems, we adjust the optimization problem to:Note: When ξ>1, it is possible to allow the classification to be wrong, and then we add the ξ as a penalty to the target function.Using Lagrange duality again, we get the duality problem as:Surprisingly, after adding the L1 regularization item, only a αi≤c is added to the like limit in the dual problem. Note that the b* calculation needs to be changed (see Platt's paper)KKT d

Hope to learn the gospel of the Children of Learning machine, the world's largest AI company Google launched a "machine learning Crash Course", not only the whole Chinese, but also free to listen to OH. The course is 15 hours, th

. DrawingT=[0:0.01:0.98]Y1=sin (2*pi*t)Plot (t,y1) % drawingOnY2=cos (2*pi*t)Plot (T,y2, ' R ')Xlabel (' time ')Ylabel (' value ')Legend (' Sin ', ' cos ') % legendTitle (' My Plot ')Print-dpng ' myplot.png ' % saved as picture fileClose % Closes the current diagramFigure (1) % Create a diagramCLF % Empty chart Current ContentsSubplot (1,2,2) % graph cut to 1*2 grid, draw 2nd gridAxis ([0.5 1-1 1]) % axis changed to x belongs to [0.5,1],y belonging to [ -1,1]Imagesc (The Magic ()), Colorbar,colo

full implementation of multi-layered neural network recognition picture of the cat Original Coursera Course homepage, in the NetEase cloud classroom also has the curriculum resources but no programming practice. This program uses the functions completed in the last job, fully implementing a multilayer neural network, and training to identify whether there is a cat in the picture. There is no comment in t

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

IntroductionThe systematic learning machine learning course has benefited me a lot, and I think it is necessary to understand some basic problems, such as the category of machine learning algorithms.Why do you say that? I admit th

and the contrast divergence algorithm, and is also an active catalyst for deep learning. There are videos and materials .L Oxford Deep LearningNando de Freitas has a full set of videos in the deep learning course offered in Oxford.L Wulide, Professor, Fudan University. Youku Video: "Deep learning

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 Support vector machine), clust

build a model from a browser. Pick out a platform and use it when you actually learn machine learning. Do not talk on paper, to practice!Video Courses Videos CourseMany people start to learn from the machine through video resources. I saw a lot of video resources related to machine

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 Support vector machine), clust

Preface: "The foundation determines the height, not the height of the foundation!" The book mainly from the coding program, data structure, mathematical theory, data processing and visualization of several aspects of the theory of machine learning, and then extended to the probability theory, numerical analysis, matrix analysis and other knowledge to guide us into the world of

The topic of machine learning techniques under this column (machine learning) is a personal learning experience and notes on the Machine Learning Techniques (2015) of

have been standing behind the scenes, and some things all the ins and outs only I know, because I and Dr. Huanghai, NetEase Cloud class, Professor Wunda and Coursera GTC translation platform, Deeplearning.ai official have had exchanges, so I still have to leave something as a description, Save everyone in the network every day noisy ah did not calm down to study seriously. As mentioned in this article, I have a chat record to support, some of the auth

) The principle of big data Large data rationale Large amounts of data can greatly improve the final performance of the learning algorithm, rather than whether you use more advanced algorithms, etc., so there is a sentence: "It's not a who had the best algorithm that wins. It's Who's have the most data. Of course, based on the two-point premise hypothesis: 1. Assume that the characteristics of the sample ca

offline workshop, base camp, or university course? Here are some links to online education sites on logical analysis, big data, data mining, and data science: Collection types of dynamic links. We also recommend some online courses-Coursera courses from Udacity: machine learning and Data Processing Analyst tutorial Na

Videos CourseMany people start to learn from the machine through video resources. I saw a lot of video resources related to machine learning on YouTube and Videolectures. The problem with this is that you may just watch the video and not actually do it. My suggestion is that when you watch the video, you should take more notes, and then you will discard your not

Learning, cs229tStatistical learning theory, cs231nconvolutional neural Networks for Visual recognition,cs231acomputer Vision:from 3D recontruct to recognition,cs231bThe cutting Edge of computer Vision,cs221Artificial Intelligence:principles Techniques,cs131computer vision:foundations and Applications,cs369lA Theoretical perspective on machine

Learning notes for "Machine Learning Practice": two application scenarios of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- After learning the implementation of the k-Nearest Neighbor Algorithm, I tested the k-

rigorously, because one of the objective functions in statistical learning is to maximize the prediction of the correct expected probability, we only consider the common loss function. Loss function is an important index to approximate the quality of the model, the greater the value of the loss function is, the greater the prediction error of the model, so what we need to do is to update the parameters of the model and minimize the value of the loss

Learning Guide for machine learning beginners (experience sharing)2013-09-21 14:47I computer research two, the professional direction of natural language processing, individuals interested in machine learning, so began to learn. So, this guy is a rookie ... It is because of