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Against the sample machine learning _note1_ machine learning

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

Three skills principles in machine learning basics of machine learning

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

Notes of machine Learning (Stanford), Week 6, Advice for applying machine learning

are as follows:Lambda Train error Validation error 0.000000 0.173616 22.066602 0.001000 0.156653 18.597638 0.003000 0.190298 19.981503 0.010000 0.221975 16.969087 0.030000 0.281852 12.829003 0.100000 0.459318 7.587013 0.300000 0.921760 1.000000 2.076188 4.260625 3.000000 4.901351 3.822907 10.000000 16.092213 9.945508 Training errors, cross-validation errors, and relationships between lambda graphs are represented as follows:When th

Machine learning practices in python3.x and python machine learning practices

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

Stanford Machine Learning Open Course Notes (8)-Machine Learning System Design

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

Machine Learning (11)-Common machine learning algorithms advantages and disadvantages comparison, applicable conditions

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

"Machine Learning Series" New Lindahua recommended Books for the machine learning community

Recommended BooksHere is a list of books which I had read and feel it was worth recommending to friends who was interested in computer Scie nCE.Machine Learningpattern recognition and machine learningChristopher M. BishopA new treatment of classic machine learning topics, such as classification, regression, and time series analysis from a Ba Yesian perspective. I

Machine learning Notes (i)--Machine learning basics

1. What is machine learningMachine learning is the conversion of unordered data into useful information.The main task of machine learning is to classify and another task is to return.Supervised learning: It is called supervised learning

Professor Zhang Zhihua: machine learning--a love of statistics and computation

good implementation code has been open source for everyone to use, so SVM becomes a benchmark model of classification algorithm. For example, KPCA is a nonlinear dimensionality reduction method proposed by computer science, in fact it is equivalent to classical MDS. The latter is very early in the statistical community, but if there is no new computer industry to discover, some good things may be buried.Machine learning has now become a major trend i

Python Machine Learning Theory and Practice (5) Support Vector Machine and python Learning Theory

Python Machine Learning Theory and Practice (5) Support Vector Machine and python Learning Theory Support vector machine-SVM must be familiar with machine learning, Because SVM has alwa

Machine learning Reading Note 01 Machine learning Basics

As the name implies, the purpose of machine learning is to allow machines to have the ability to learn, understand, and comprehend things similar to human beings. Imagine how important it is for a patient's recovery if a computer can summarize and sum up a large number of cancer treatment records, and be able to give appropriate advice and advice to a physician. In addition to the medical field, financial s

Machine Learning Public Course notes (10): Large-scale machine learning

increase or reduce the number of example (change 100 to 1000 or 10, etc.), reduce or increase the learning rate.elearning (Online learning)The previous algorithm has a fixed training set to train the model, when the model is well trained to classify and return the future example. Online learning is different, it updates the model parameters for each new example,

[Machine learning Combat] use Scikit-learn to predict user churn _ machine learning

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

Stanford Machine Learning Open Course Notes (7)-some suggestions on machine learning applications

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. deciding what to try next ( Determine what to do next ) I have already introduced some machine learning methods. It is obviously not enough to know the specific process of these methods. The key is to learn how to use them. The so-called best way to master knowledge is to put it into practice. Consider the ear

Machine learning-Bayesian theory _ Machine learning

Bayesian Introduction Bayesian learning Method characteristic Bayes rule maximum hypothesis example basic probability formula table Machine learning learning speed is not fast enough, but hope to learn more down-to-earth. After all, although it is it but more biased in mathematics, so to learn the rigorous and thoroug

"Python Machine learning Time Guide"-Python machine learning ecosystem

This article focuses on the contents of the 1.2Python libraries and functions in the first chapter of the Python Machine learning Time Guide. Learn the workflow of machine learning.I. Acquisition and inspection of dataRequests getting dataPandans processing Data1 ImportOS2 ImportPandas as PD3 ImportRequests4 5PATH = R'E:/python

Manually install or upgrade VMware Tools in a Linux virtual machine

For Linux virtual machines, you can use the command-line tools to manually install or upgrade VMware tools.This time the Linux virtual machine is CentOS6.5PrerequisiteTurn on the virtual machine.Confirm that the client operating system is running.Because the VMware Tools installer is written in Perl, make sure that Perl is installed in the guest operating system.

Machine learning Getting Started report problem solving general Workflow __ Machine Learning

For a given set of data and problems, the machine learning method to solve the problem is generally divided into 4 steps: A Data preprocessing First, you must ensure that the data is in a format that meets your requirements. The standard data format can be used to fuse algorithms and data sources to facilitate matching operations. In addition, you need to prepare specific data formats for

Machine learning actual Combat reading notes (i) Machine learning basics

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

Support Vector Machine-machine learning in action learning notes

p.s. SVM is more complex, the code is not studied clearly, further learning other knowledge after the supplement. The following is only the core of the knowledge, from the "machine learning Combat" learning summary. Advantages:The generalization error rate is low, the calculation cost is small, the result is easy to ex

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