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Machine learning notes (b) univariate linear regression Note: This content resource is from Andrew Ng's machine learning course on Coursera, which pays tribute to Andrew Ng. Model representationHow to solve the problem of house price in note (a), this will be

In machine learning, are more data always better than better algorithms? No. There is times when more data helps, there is times when it doesn ' t. Probably One of the most famous quotes Defen Ding the power of data is that of Google ' s Directorpeter norvigclaiming that" We Don has better algorithms. We just has more data. ". This quote was usually linked to the article on "the Unreasonable effectiveness

Reprint: http://www.cnblogs.com/LBSer/p/4440590.htmlMachine learning model is widely used in the Internet industry, generally do machine learning applications when most of the time is spent on feature processing, one of the key step is to attribute data normalization, why should be normalized? Wikipedia gives explanations: 1 It accelerates the gradient descent to

distribution, in accordance with the joint distribution of the query, we can obtain pi.Q's design is said to be a value of 60W knife annual salary job, dare not to speculate. Here we assume that Q is given (UNIFORM/SW) **********************************************The MH sampling process is as follows:1, given assignment, according to the F to find Pi (Assignment)2, according to the above formula to calculate the acceptance probability a3, decide whether to accept, complete the sampling update

is unroll into a vector, then using the existing gradient descent algorithm in the library to find the optimal parameters, and finally reshape into a matrix form; The reason for this is that the parameters of the ready-made gradient descent algorithm, the Inittheta requirement, must be in the form of a vector.3,gradient CheckingThis is a mathematical method to seek partial derivative.It can be used to verify that the gradient descent algorithm is implemented correctly, when the data of the two

This series of articles is the study notes of "machine learning", by Prof Andrew Ng, Stanford University. This article is the notes of week 5, neural Networks learning. This article contains some topic on cost Function and backpropagation algorithm.Cost Function and BackPropagationNeural networks is one of the most pow

The basis of text sentiment analysis is natural language processing, affective dictionary, machine learning method and so on. Here are some of the resources I've summed up.Dictionary resources:Sentiwordnet"Knowledge Network" Chinese versionChinese Affective polarity dictionary NTUSDEmotion Vocabulary Ontology DownloadNatural language processing tools and platforms:Institute of Social Computing and Informati

normal equations omit the step of feature scaling when dealing with multivariable regression equations, simply follow the steps of a single variable and be more concise.Three, the choice of learning rateThe efficiency of gradient descent is greatly influenced by the learning rate, which is too small, the convergence rate is very slow, and the number of iterations is increased; when too large, each iteratio

Machine Learning Day No. 0Welcome reprint, please indicate the source (Http://blog.csdn.net/tonyshengtan), respect for labor, respect for knowledge, welcome to discuss.The opening crap.Back to write a blog, although always know that learning is not the end, but still will doubt, learn to what extent can find a job like this (spit groove: The work is too disgustin

is: Understanding-Bayesian model.http://www.merl.com/people/brand/Merl (Mitsubishi Electric Laboratory) specializes in "Style machine".http://research.microsoft.com/~ablake/A.Blake, a highly prestigious CV, graduated from Cambridge University in 1977 with a bachelor's degree in mathematics and electronic science from 31 College. After that, he set up a research group in Mit,edinburgh,oxford and became Professor of Oxford until 1999, when he entered t

------------------------------------------------------------------------------------------------------------------ This article is excerpted from the courseware "Machine Learning" by Andrew Ng of Stanford University. Why is the Department of biological engineering at Ocean University the best? They are incomparable to other schools in studying the environment a

Brief introduction:This section describes the algorithms in the 12th and 13 episodes of the Stanford Machine learning public class: K-means algorithm, Gaussian mixture model (GMM). (9, 10, 11 episodes do not introduce, skip the ha)First, K-means algorithmIt belongs to unsupervised learning clustering algorithm, given a

From the phenomenon of a life: when we installed, we will not install more than one decompression software, do not want to be inexplicably installed additional butler. In contrast, we will install a variety of players. So, what is this for? Of course, you can also think of such a problem, praise the software so much, hard disk is big enough, why not all installed? Seeing the second question, the idea seems clear. Very simple, decompression software, Butler function is similar, and all free, even

What is history, history is us, not you, not him, not her, is all people.—————————— PrefaceThis article is a summary of Bo Master's reading about Bayes and its related knowledge.first, the mathematical beauty of the article: the ordinary and magical Bayesian methodtheory and practice of machine learning (III.) naive Bayesianthree, from the Bayesian approach to the Bayesian networkFour, the beauty of mathema

guesses, and certainly not very accurate at first. But based on this speculation, it can be calculated that each person is more likely to be male or female distribution. For example, a person's height is 1.75 meters, obviously it is more likely to belong to the male height of this distribution. Accordingly, we have a attribution for each piece of data. Then, according to the maximum likelihood method, the parameters of male height normal distribution are re-estimated by these several data which

, or even smaller after a very small concatenation, or even roundOff 0. This will affect the judgment, so they will be transferred to the logarithm space for calculation, the logarithm is often used in machine learning, to avoid ambiguity caused by the numerical operation while keeping monotonous, in addition, the logarithm can be used to convert multiplication to addition to accelerate the operation. There

as a basis for dividing two classes. In combination with P/R analysis, the selection of threshold value can be more flexible and excellent.As you can see, if you choose a threshold that is too low, more test samples will be divided into 1 categories. Therefore, the recall rate can be improved, obviously the accuracy of the sacrifice of the corresponding accuracy rate.In this example, perhaps I would choose 0.42 as the dividing value-because the accuracy and recall rates are high.Finally, give s

Tags: get attention to bin www. Command line nbsp PAC Read Write codeRecently began to look at Coursera above the machine learning course, the above mentioned a software--octave, so I transferred the following blog.Do not know what is the specific reason, I download octave-4.2.1-w64-installer.exe, the speed is extremely slow, so downloaded Octave-4.2.1-w64.zip, a

It is mentioned in this series that using Python to start machine learning (3: Data fitting and generalized linear regression) mentions the regression algorithm for numerical prediction. The logical regression algorithm is essentially regression, but it introduces a logical function to help classify it. The practice found that the logical regression in the field of text classification performance is also ve