machine learning code example

that the neural network can get better test performance when the same task is performed on the unknown test data set.2, Problem formalizationTaking the common classification task as an example, the learning problem of Feedforward network is formalized. The dimension and range of input and output of feedforward network are determined by the characteristics of practical application problems. As shown in 2, i

　　Preface In recent weeks, I spent some time learning the machine learning course of the Dragon Star program for the next summer vacation. For more information, see the appendix. This course chooses to talk about the basic model in ml. It also introduces popular and new algorithms in recent years. In addition, it also combines ml theory with actual problems, for

discriminant models (discriminative model)The generation method is obtained by the data Learning Joint probability distribution P (x, y) and then the conditional probability distribution P (y| X) as the predictive model, the model is generated : P (Y |X )= P(X,Y)p ( X ) This method is called a build method , which represents the generation relationship of output y produced by a given input x. such as: Naive Bayesian and Hidden M

determine its color, This kind of ball can be called probability (probabilistic) ball. corresponding to machine learning, is a sample of noise, that is, not sure, where the mark Y obeys the probability distribution, this form is called the target distribution (target distribution) instead of the target function, this method is called the Generation method.Why this is called the target distribution, give a

difference is far from the real curve (Green Line). Here is an over-fitting problem ), it can be said that it is a very important issue in machine learning. Root-mean-square error We can see the example in Figure 1.5. After M reaches a certain stage, the error on the test data will increase significantly. We understand it as overfitting! Let's briefl

). Here is an over-fitting problem ), it can be said that it is a very important issue in machine learning. Root-mean-square error We can see the example in Figure 1.5. After M reaches a certain stage, the error on the test data will increase significantly. We understand it as overfitting! Let's briefly discuss over-fitting. There are many factors that cause ov

This article is reproduced from: http://www.csdn.net/article/2015-10-01/2825840 Absrtact: Deep learning based on Hadoop is an innovative method of deep learning. The deep learning based on Hadoop can not only achieve the effect of the dedicated cluster, but also has a unique advantage in enhancing the Hadoop cluster, distributed depth

This series of blogs records the Stanford University Open Class-Learning notes for machine learning courses.Machine learning DefinitionArthur Samuel (1959): Field of study that gives computers the ability to learn without being explicitly programmed.Tom Mitchell (1998): A computer program was said to learn from experie

data (such as which friends and you hit it off). From the above example, we can see that machine learning is actually the imitation of human intelligence, but also the way to achieve human and higher intelligence.(What's the goods?) What does he basically have?(rather difficult machine

Python is widely used in scientific computing: Computer vision, artificial intelligence, mathematics, astronomy, etc. It also applies to machine learning. This article lists and describes Python's wide application in Scientific Computing: Computer vision, artificial intelligence, mathematics, astronomy, etc. It also applies to machine

instruction represents one or more operations. Give a simple example, and you can use it in your life. Now make a small game, a on the paper randomly wrote a 1 to 100 integer, b to guess, guess the game is over, guess the wrong word a will tell B guess small or big. So what will b do, the first time you must guess 50, guess the middle number. Why is it? Because this is the worst case (log2100log2100) can be guessed six or seven times. This is a binar

will find nothing to say, just give a lot of examples. Algorithms of the Intelligent Web (Smart Web algorithm) PDF138Author Haralambos Marmanis, Dmitry Babenko. The formula in this book is a little bit more than "collective intelligence programming", the example of which is mostly the application on the Internet, to see the name. The disadvantage is that the matching code inside is BeanShell and not pyth

solving the parameters can be accomplished by the optimization algorithm. In the optimization algorithm, the gradient ascending algorithm is the most common one, and the gradient ascending algorithm can be simplified to the random gradient ascending algorithm.2.2 SVM (supported vector machines) Support vectors machine:Advantages: The generalization error rate is low, the calculation cost is small, the result is easy to explain.Cons: Sensitive to parameter adjustment and kernel function selectio

using HMMs to answer one of the most fundamental questions in Biology–how are DNA, The Code of Life, translated to physical or behavioral attributes of an organism? The course is comprised of videos and runs a total time of 4 hours. It currently averages a 4.7/5 user rating. However, the course isn't free, it costs $.9. DATA Science and machine learning wi

Original writing, reproduced please indicate the source of http://www.cnblogs.com/xbinworld/archive/2013/04/25/3041505.html Today I will start learning pattern recognition and machine learning (PRML), Chapter 1.2, probability theory (I) This section describes the essence of probability theory in the entire book, highlighting an uncertainty understanding

Original: Image classification in 5 MethodsAuthor: Shiyu MouTranslation: He Bing Center Image classification, as the name suggests, is an input image, output to the image content classification of the problem. It is the core of computer vision, which is widely used in practice. The traditional method of image classification is feature description and detection, such traditional methods may be effective for some simple image classification, but the traditional classification method is overwhelmed

. such as stock forecasts. Clustering (clustering): Data is not labeled, but there are some similarity metrics that can be used to classify data according to these criteria. For example, in a pile of photos that do not give a name, the photos of the same person are automatically gathered together. Rule extraction: Discover the statistical relationships between attributes in the data, not just predict things. such as beer and diapers.

practical application of CNN.1, convolutional neural network training algorithm simulationAlgorithm 1: Training algorithm of convolutional neural network based on BP algorithmInput: Training Sample {xn,tn}n=1n, convolutional neural network structure {hl}l=1l, learning rate ηOutput: Parameters of convolutional neural networksTraining process:Initialize: Sets the convolution core and offset of all the layers of the convolutional network to a smaller ra

. Most machine learning algorithms involve training and predicting two parts, training models, and predicting unknown samples. The same is true for machine learning packages in spark.Spark divides the machine learning algorithm in

First, Introduction1. Concept : The field of study that gives computers the ability to learn without being explicitly programmed. --an older, informal definition by Arthur Samuel (for tasks that cannot be programmed directly to enable the machine to learn) "A computer program was said to learn from experience E with respect to some class of tasks T and performance measure P, if Its performance on tasks in T, as measured by P, improves wit