machine learning sample code

; Data homogeneous: The range of values for all features is the same.Handling Missing valuesOverall, the missing value is populated with 0 (assuming that 0 is not meaningful) and is feasible for neural networks. The model then automatically learns that 0 represents the missing value, and then ignores 0.Note If the training data for the model has no missing values, and the test set has missing values, the model cannot learn to ignore 0 values. In this case, you should manually generate a trai

Deep Learning SpecializationWunda recently launched a series of courses on deep learning in Coursera with Deeplearning.ai, which is more practical compared to the previous machine learning course. The operating language also has MATLAB changed to Python to be more fit to the current trend. A study note on this series o

Python3 Learning using the APIPrediction of two kernel function models for support vector machinesGit:https://github.com/linyi0604/machinelearning fromSklearn.datasetsImportLoad_boston fromSklearn.cross_validationImportTrain_test_split fromSklearn.preprocessingImportStandardscaler fromSklearn.svmImportSVR fromSklearn.metricsImportR2_score, Mean_squared_error, Mean_absolute_errorImportNumPy as NP#1 Preparing Data#Read the Boston area rate informationBo

First, how to learn a large-scale data set?In the case of a large training sample set, we can take a small sample to learn the model, such as m=1000, and then draw the corresponding learning curve. If the model is found to be of high deviation according to the learning curve, the model should continue to be adjusted on

Draw a map, there is the wrong place to welcome correct:In machine learning, features are critical. These include the extraction of features and the selection of features. They are two ways of descending dimension, but they are different:feature extraction (Feature Extraction): creatting A subset of new features by combinations of the exsiting features. In other words, after the feature extraction A feature

steepness factor to these nonlinear functions, adjust the saturation region of the nonlinear function, adjust the shape of the training loss function, and adjust the parameter adjustment out of the saturated area.For the sigmoid function, the steepness factor (recorded as λ) can be set as follows: Δs (x) =1/(1+exp (-x/λ))2.1.4 Using numerical optimization techniquesIn order to improve the convergence speed and stability of neural network training, we can also use the numerical optimization algo

= max):M,n = shape (data) w = ones (n) forIinchRange (numiter): Dataindex = Range (m) forJinchRange (m): Alpha =4/ (1.0+ i + j) +0.01randindex = Int (Random.uniform (0, Len (dataindex))) H = sigmoid (sum (data[randindex] * w)) error = Label[randindex]-H w = W + Alpha * error* Array (Data[randindex])del(Dataindex[randindex])returnWWNEWADV = stocgradascentadvanced (data, label, Numiter = Max) Plotbestsplit (WNEWADV)After using the optimization strategy, the variation of the parameters of each reg

Tags: introduction baidu machine led to the OSI day split data setI. Introduction TO MACHINE learning Defined 　　The machine learning definition given by Tom Mitchell: For a class of task T and performance Metric p, if the computer program is self-perfecting wit

linear kernel)The neural network works well in all kinds of n, m cases, and the defect is that the training speed is slow.Reference documents[1] Andrew Ng Coursera public class seventh week[2] Kernel Functions for machine learning applications. http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applicati

the output4) due to random sampling, the variance of the trained model is small and the generalization ability is strong.5) The algorithm is easier to implement than boosting.6) Insensitive to partial feature deletionsMain disadvantages of random forests:1) In some large noisy sample sets, the RF model is prone to fall into the fit2) The characteristics of the value ratio are easy to influence the decision of random forest, and affect the fitting eff

network learning): Http://52opencourse.com/289/coursera Public Lesson Video-Stanford University Nineth lesson on machine learning-neural network learning-neural-networks-learningStanford Deep Learning Chinese version: Http://deeplearning.stanford.edu/wiki/index.php/UFLDL tu

collaborative computing that is popular. Finally, I would like to introduce how to use this data with a large amount of data to build a very intelligent system, making our system more intelligent. We all know that statistical machine learning is based on data. The most important step is to collect and collect data. High-quality and large-scale data can help us build a very intelligent system. There is a ve

Brief introductionMachine learning algorithms are algorithms that can be learned from data and improved from experience without the need for human intervention. Learning tasks include learning about functions that map input to output, learning about hidden structures in unlabeled data, or "instance-based

imagenet by deep learning, and the deep learning model, represented by CNN, is now a bit exaggerated, borrowed from the Chinese University of Hong Kong Prof. Xiaogang Wang Teacher's summary article, Deep learning is nothing more than the traditional machine feature learning

Brief introductionBefore I introduce machine learning, I would like to start by listing some examples of machine learning: junk e-mail detection: Identifies what is spam and what is not, based on the messages in the mailbox. Such a model can help categorize spam and non-spam messages by programs. This example

Stanford University's Machine learning course (The instructor is Andrew Ng) is the "Bible" for learning computer learning, and the following is a lecture note.First, what is machine learningMachine learning are field of study that