machine learning buzzwords

Machine Learning 4, machine learning Probability-based classification method: Naive BayesBayesian decision theory Naive Bayes is a part of Bayesian decision-making theory. Therefore, before explaining Naive Bayes, let's take a quick look at Bayesian decision-making theory knowledge. The core idea of Bayesian decision-m

In machine learning, often need to calculate the distance between each sample, used for classification, according to distance, different samples grouped into a class; But in the current machine learning algorithm, the distance calculation mode is endless, then this blog is mainly to comb the current

A brief introduction to Learning _note1 against Sample machine Machine learning methods, such as SVM, neural network, etc., although in the problem such as image classification has been outperform the ability of human beings to deal with similar problems, but also has its inherent defects, that our training sets are fe

The Ames Razor principle (Occam ' s Razor)One sentence is said, "an explanation of the data should is mad as simple as possible,but no simpler".The meaning of machine learning is that the simplest explanation of the data is the best explanation (the simplest model, fits the data is also and the most plausible).For example, the picture above, the right is not better than the left to explain? That's obviously

Learning plan diagram of actual Java Virtual Machine (Understand Java Virtual Machine), Java Virtual Machine I don't want to talk about it anymore. I am actually using a Java virtual machine. I have to study hard and get started every day! Develop a

Machine learning practices in python3.x and python machine learning practices Machine Learning Practice this book is written in the python2.x environment, while many functions and 2 in python3.x. the names or usage methods in x ar

findF1scoreThe algorithm with the largest value. 5. Data for Machine Learning ( Machine Learning data ) In machine learning, many methods can be used to predict the problem. Generally, when the data size increases, the accura

1. Decision Tree　　applicable conditions: The data of different class boundary is non-linear, and by continuously dividing the feature space into a matrix to simulate. There is a certain correlation between features. The number of feature values should be similar, because the information gain is biased towards more numerical characteristics.　　Advantages: 1. Intuitive decision-making rules; 2. Nonlinear characteristics can be handled; 3. The interaction between variables is considered.　　Disadvanta

how the language works. You'll start to recognize patterns and you'll start to expect certain sounds. When you do get hold of a dictionary or a tutor then you'll learn the language much quicker. One of the buzzwords that we hear from companies like Google and Facebook is ' neural Net. ' The key thing about unsupervised learning are that once the unlabeled data have been processed it only take

make life easier than ever.Note: Intelligent chat robots (with artificial intelligence) are also rapidly emerging. However, it is necessary to be vigilant-because the deviations in the training data set can cause serious damage to the user experience. Microsoft's ' Tay ' chat robot is a classic example of this failure.Developers will focus on using machine learning to solve more "real problems"When it come

Customer Churn "Loss rate" is a business term that describes the customer's departure or stop payment of a product or service rate. This is a key figure in many organizations, as it is usually more expensive to get new customers than to retain the existing costs (in some cases, 5 to 20 times times the cost). Therefore, it is invaluable to understand that it is valuable to maintain customer engagement because it is a reasonable basis for developing retention policies and implementing operational

Simple examples are used to understand what machine learning is, and examples are used to understand machine learning. 1. What is machine learning? What is machine

Original: http://blog.csdn.net/abcjennifer/article/details/7834256This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vector machines), clustering, dimensionality reduc

Original handout of Stanford Machine Learning Course This resource is the original handout of the Stanford machine learning course, which is AndrewNg said that a total of 20 PDF files cover some important models, algorithms, and concepts in machine

http://sourceforge.net/projects/numpy/files/download the corresponding version of the NumPy, everywhere, find a not python2.7Use Pip, please.Pip Install NumPyDownload finished, the hint does not install C + +, meaning is also to install VS2008, but installed is VS2012, had to download a VC for Pythonhttp://www.microsoft.com/en-us/download/confirmation.aspx?id=44266Re-pip, wait for the most of the day, the final count is successfulInput command introduced NumPyFrom numpy Import *Operation:InputRa

1. Common steps 2. Chinese participle 1 This is relative to the English text affective analysis, Chinese unique preprocessing. 2 Common methods: Based on the dictionary, rule-based, Statistical, based on the word annotation, based on artificial intelligence. 3 Common tools: Hit-language cloud, Northeastern University Niutrans statistical Machine translation system, the Chinese Academy of Sciences Zhang Huaping Dr. Ictclas, Posen technology, stutterin

Machine learning is accelerating the pace of progress, it is time to explore this issue. Ai can really protect our systems in the future against cyber attacks. Today, an increasing number of cyber attackers are launching cyber attacks through automated technology, while the attacking enterprise or organization is still using manpower to summarize internal security findings, and then compare them with exter

some time ago on the Internet to see the Coursera Open Classroom Big Machine learning Cornerstone Course, more comprehensive and clear machine learning needs of the basic knowledge, theoretical basis to explain. There are several more important concepts and ideas in foundation, first review, and then open the follow-up

to the right in this image. We can generally see the two learning curves, the two curves of blue and red are approaching each other. Therefore, if we extend the curve to the right, it seems that the training set error is likely to increase gradually. The cross-validation set error will continue to decline. Of course, we are most concerned with cross-validation set errors or test set errors. So from this picture, we can basically predict that if we co

1. Vector Norm Norm, Norm, is a concept similar to "Length" in mathematics, which is actually a kind of function.The regularization (regularization) and sparse coding (Sparse coding) in machine learning are very interesting applications.For Vector a∈rn A\in r^n, its LP norm is | | a| | p= (∑IN|AI|P) 1p (1) | | a| | _p= (\sum_i^n |a_i|^p) ^{\frac 1 p} \tag 1Commonly used are: L0 NormThe number of elements i