Reprint Please specify the Source: http://www.cnblogs.com/ymingjingr/p/4271742.htmlDirectoryMachine learning Cornerstone Note When machine learning can be used (1)Machine learning Cornerstone Note 2--When you can use machine
First, the foregoingNumPy(numerical python abbreviation) is an open source Python Scientific Computing Library. Use NumPy , you can use arrays and matrices in a very natural way . Numpy contains many useful mathematical functions, including linear algebra operations, Fourier transforms, and random number generation functions . The Library's predecessor was a library for array operations that began in 1995 years. After a long period of development, it has basically become the most basic Python
of the current node is the middle half of the distance of all its leaf nodes is float (NUMLEAFS)/2.0/plottree.totalw* 1, but since the start Plottree.xoff assignment is not starting from 0, but the left half of the table, so also need to add half the table distance is 1/2/plottree.totalw*1, then add up is (1.0 + float (numleafs))/2.0/ Plottree.totalw*1, so the offset is determined, then the X position becomes Plottree.xoff + (1.0 + float (numleafs))/2.0/PLOTTREE.TOTALW3, for Plottree function p
learning is a discipline that studies how to use machines to simulate human learning activities. Machine Learning is a learning that studies machines to acquire new knowledge and new skills and to recognize existing knowledge. The "mach
, people may have skin color, height, physique and ... Hey, I'm evil. And so on, are these features independent of each other? Of course not, such as the black average height is not white high, there are black people running ability and so on, characteristics and characteristics are related. But naive Bayesian sees them as independent.
In principle, naive Bayes has an objective minimum error rate because it requires the least number of parameters. But
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
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
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
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 Coursera public
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
1. The complete course of statistics all of statistics Carnegie Kimelon Wosseman
2. Fourth edition, "Probability Theory and Mathematical Statistics" Morris. Heidegger, Morris H.degroot, and Mark. Schevish (Mark j.shervish)
3. Introduction to Linear algebra, Gilbert. Strong--Online video tutorials are classic
4. "Numerical linear algebra", Tracy Füssen. Lloyd and David. Bao
Textbooks suitable for undergraduates
5. Predictive data analysis of
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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
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
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
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
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
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
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
Stanford cs231n 2017 newest Course: Li Feifei Detailed framework realization and comparison of depth learning by Zhuzhibosmith June 19, 2017 13:37
Stanford University Course cs231n (convolutional Neural Networks for visual recognition) is widely admired in academia as an important foundation course in depth
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