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Professor Zhang Zhihua: machine learning--a love of statistics and computationEditorial press: This article is from Zhang Zhihua teacher in the ninth China R Language Conference and Shanghai Jiaotong University's two lectures in the sorting out. Zhang Zhihua is a professor of computer science and engineering at Shanghai Jiaotong University, adjunct professor of data Science Research Center of Shanghai Jiaot
first, gradient descent method In the machine learning algorithm, for many supervised learning models, the loss function of the original model needs to be constructed, then the loss function is optimized by the optimization algorithm in order to find the optimal parameter. In the optimization algorithm of machine
AI Bacteria Perceptron is one of the oldest classification methods, and today it seems that its classification model is not strong in generalization at most, but its principle is worth studying. Because the study of the Perceptron model, can be developed into support vector machine (by simply modifying the loss function), and can develop into a neural network (by simply stacking), so it also has a certain position. So here's a brief introduction to
formed a more perfect experience accumulation of the application scene. There are many applications in data mining that need to be developed, even if it is possible to dig out valuable patterns. Like Recommender systems, computer vision, and NLP, these values are known to be more fortunate than others. Write the Book of course everything to write, is there something in machine
the file name of the data to iris.csv. The Code is as follows: 1 Is it easy? Just 12 lines of code is enough. Next, let's test it. According to the figure above, when we input 5 3.3 1.4 0.2, the output should be Iris-setosa. Let's take a look: Check that at least one original data is input and the correct result is obtained. But what if we enter data that is not in the original dataset? Let's test two groups: From the data of the two images we posted earlier, the data we input does not exist
(i) Recognition of the returnRegression is one of the most powerful tools in statistics. Machine learning supervised learning algorithm is divided into classification algorithm and regression algorithm, in fact, according to the category label distribution type is discrete, continuity and defined. As the name implies, the classification algorithm is used for disc
there is no prior knowledge, the Gaussian kernel is generally chosen. Why choose a Gaussian nucleus? Because you can map data to an infinite-dimensional space.Minimum optimization of the SMO sequenceThis learning method is to simply solve the parameters of the SVM algorithm, is not very important (change-^-^), so there is no very detailed look, later have time to read and then update to this article.Pending Update:Reference books:The method of statis
Machine learning Algorithms and Python Practice (ii) Support vector Machine (SVM) BeginnerMachine learning Algorithms and Python Practice (ii) Support vector Machine (SVM) Beginner[Email protected]Http://blog.csdn.net/zouxy09Machine lear
) Discriminant analysis is mainly in the statistics over there, so I am not very familiar with the temporary find statistics Department of the Boudoir Honey made up a missed lesson. Here we are now learning to sell. A typical example of discriminant analysis is linear discriminant analysis (Linear discriminant analyses), referred to as LDA. (notice here not to be confused with the implied Dirichlet distribution (latent Dirichlet allocation), although
Course Description: This is an introductory course on deep learning, and deep learning is mainly used for machine translation, image recognition, games, image generation and more. The course also has two very interesting practical
algorithm, neural network based algorithm and so on. Of course, the scope of machine learning is very large, and some algorithms are difficult to classify into a certain category. For some classifications, the same classification algorithm can be used for different types of problems. Here, we try to classify commonly used algorithms in the easiest way to underst
Machine learning Algorithm and Python Practice (c) Advanced support vector Machine (SVM)Machine learning Algorithm and Python Practice (c) Advanced support vector Machine (SVM)[Email protected]Http://blog.csdn.net/zouxy09Machine
Android Virtual Machine Learning summary Dalvik Virtual Machine Introduction 1. The most significant difference between a Dalvik virtual machine and a Java virtual machine is that they have different file formats and instruction sets. The Dalvik virtual
randomly groups the data to the extent that training intensive accounts for 70% of the original data (this ratio can vary depending on the situation), and the test error is used as the criterion when selecting the model. The question comes from the Stanford University Machine Learning course on Coursera, which is des
prediction Naturual Language Processing Coursera Course Book on NLP NLTK NLP W/python Foundations of statistical Language processing Probability Statistics Thinking Stats-book + Python Code From algorithms to Z-scores-book The Art of R Programming-book (not finished) All of Statistics Introduction to statistical thought Basic probability theory I
ObjectiveFor deep learning, novice I recommend to see UFLDL first, do not do assignment words, one or two nights can be read. After all, convolution, pooling what is not a particularly mysterious thing. The course is concise, sharply, and points out the most basic and important points.cs231n This is a complete course, the content is a bit more, although the
average of the recent x days, this x is how much, there is a calculation method is β X is equal to 1/e, in order to find X, that actually this x is 1/(1-β).In addition, Ng points out that the exponential weighted average is not the best, nor is it a precise way to calculate the average, but it does not need to keep all of the recent data and consumes less memory, which is a good way of doing it efficiently.3 deviation correction of the exponential weighted average (Bias correction in exponentia
place is different, for example, in quite a detailed introduction of neural network theory of the rise and fall. So I strongly suggest you look at yourself again and don't forget the links inside the link to other places. By the way, Xu 's classmate intends to find time to translate this article, this is a fairly long article, see the E-text waiting to see translation:)The second one is " ai " (Artificial Intelligence). Of course, there are
Self-study machine learning three months, exposure to a variety of algorithms, but many know its why, so want to learn from the past to do a summary, the series of articles will not have too much algorithm derivation.We know that the earlier classification model-Perceptron (1957) is a linear classification model of class Two classification, and is the basis of later neural networks and support vector machin
Hello everyone, I am mac Jiang, today and everyone to share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-Job three q6-10 C + + implementation. Although there are many great gods in many blogs have given the implementation of Phython, but given the C + + implementation of the article is
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