parameter sweep machine learning

Objective:This series is in the author's study "Machine Learning System Design" ([Beauty] willirichert) process of thinking and practice, the book through Python from data processing, to feature engineering, to model selection, the machine learning problem solving process one by one presented. The source code and data

Burak KanberTranslation: Wang WeiqiangOriginal: http://burakkanber.com/blog/machine-learning-in-other-languages-introduction/ The genetic algorithm should be the last of the machine learning algorithms I came into contact with, but I like to use it as a starting point for this series of articles, because this alg

This topic (Machine Learning) including Single-parameter linear regression, multi-parameter linear regression, Octave tutorial, logistic regression, regularization, neural network, machine learning system design, SVM (Support Vect

extracting some column rules from it is stronger than KNN.Disadvantages:1. easy to fit;2. For data with inconsistent sample numbers, the results of information gain in decision trees are biased towards those with more numerical values.3. It is difficult to deal with information when it is missing. The dependency between attributes in the dataset is ignored.SVMAdvantages:1. Can be used for linear/non-linear classification, can also be used for regression, the generalization error rate is low, th

information gain Building a decision Tree Random Forest K Nearest neighbor--an algorithm of lazy learning Summarize The fourth chapter constructs a good training set---data preprocessing Handling Missing values Eliminate features or samples with missing values Overwrite missing values Understanding the Estimator API in Sklearn Working with categorical data Splitting a dataset in

Summary of machine learning problems Category Name Keywords Supervised Classification Decision tree Information Gain Classification regression tree Gini index, Gini 2 Statistics, pruning Naive Bayes Non-parameter estimation, Bayesian Estimation Linear Discriminant Analysis Fishre identification, fe

+ 1 parameter: x0 -- x256. We hope to use machine learning to determine the values of all these parameters. However, with so many parameters, machine learning may take a lot of time to complete, and the effect is not necessarily good. We can see that some pixels are not nee

-caaaomvweity052.png-wh_50 "/>For the above, we need to solve two sets of parameters, according to the previous experience of machine learning, we can cross, that is, to fix a set of parameters, solve another group, and then optimize another group. First, the parameter class X is derivative and the result is 0, we have:650) this.width=650; "Src=" https://s1.51cto

each parameter corresponding to 44 is the value of J_vals (i,j) end46 end47 j_vals = J_vals ';% Surface plot49 Figure;50 Surf (theta0_vals, theta1_vals, j_vals)% draws an image of the parameter and loss function. Pay attention to use this surf compare egg ache, surf (x, y, z) is such, Wuyi%x,y is a vector, Z is a matrix, with X, Y paved grid (100*100 point) and Z of each point 52 to form a graph, but how t

to determine, easy to get into local minima, there are learning phenomena, these defects in the SVM algorithm can be well solved.Source: Http://www.cnblogs.com/zhangchaoyangA summary of machine learning problem methods Big class Name Keywords Supervised classification Decision Tree Information gain Ca

. However, there is a better neural network model, which is the restricted Boltzmann machine. The method of using Cascade Boltzmann machines to form deep neural networks is called deep belief network DBN in deep learning, which is a very popular method at present. In the following terms, the self-associative network is called the Self-coding network Autoencoder. By cascading the deep network of self-coded n