machine learning containers

First, the visualization method Bar chart Pie chart Box-line Diagram (box chart) Bubble chart Histogram Kernel density estimation (KDE) diagram Line Surface Chart Network Diagram Scatter chart Tree Chart Violin chart Square Chart Three-dimensional diagram Second, interactive tools Ipython, Ipython Notebook plotly Iii. Python IDE Type Pycharm, specifying a Java swing-based user interface PyDev, SWT-based

Course Description:This lesson focuses on the things you should be aware of in machine learning, including: Occam's Razor, sampling Bias, and Data snooping.Syllabus: 1, Occam ' s razor.2, sampling bias.3, Data snooping.1, Occam ' s Razor.Einstein once said a word: An explanation of the data should is made as simple as possible, but no simpler.There are similar sayings in software engineering:Keep It simple

SVM is a widely used classifier, the full name of support vector machines , that is, SVM, in the absence of learning, my understanding of this classifier Chinese character is support/vector machines, after learning, Only to know that the original name is the support vector/machine, I understand this classifier is: by the sparse nature of a series of support vecto

11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data11.1 what to do firstThe next video will talk about the design of the machine learning system. These videos will talk about the major problems you will encounter when desi

2019 Machine Learning: Tracking the path of AI developmentHttps://mp.weixin.qq.com/s/HvAlEohfSEJMzRkH3zZtlwThe time has come to "guide" the "Smart assistant". Machine learning has become one of the key elements of the global digital transformation, and in the enterprise domain, the growth of

"Python Machine learning and practice – from scratch to the road to Kaggle race" very basicThe main introduction of Scikit-learn, incidentally introduced pandas, NumPy, Matplotlib, scipy.The code of this book is based on python2.x. But most can adapt to python3.5.x by modifying print ().The provided code uses Jupyter Notebook by default, and it is recommended to install ANACONDA3.The best is to https://www.

structure as follows.What effect does this autoencoder have on machine learning?1) for supervised learning: This information-preserving NN's hidden layer structure + weight is a reasonable conversion of the original input, equivalent to learning the expression of data in the structure2) for unsupervised

train streaming data and make predictionsIn the following example, we train a perceptron to categorize the datasets of 20 news categories. This data set of 20 Web news sites collects nearly 20,000 news articles. This data set is often used for document classification and clustering experiments, and Scikit-learn provides an easy way to download and read datasets. We will train a perceptron to identify three news categories: Rec.sports.hockey, Rec.sport.baseball, and Rec.auto. Scikit-learn's perc

Learning notes of machine learning practice: Implementation of decision trees, Decision tree is an extremely easy-to-understand algorithm and the most commonly used data mining algorithm. It allows machines to create rules based on datasets. This is actually the process of machine

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

Original address: http://blog.csdn.net/google19890102/article/details/18222103The Extreme learning Machine ELM is a neural network algorithm proposed by Huangguang. The biggest feature of Elm is that the traditional neural network, especially the tow-layer feedforward neural Network (SLFNS), Elm is faster than the traditional learning algorithm.ELM is a new fast

, there are n single classifiers, each single classifier has an equal error rate, and the single classifier is independent of each other, error rate is irrelevant. With these assumptions, we can calculate the error probability of the integration model:If n=11, the error rate is 0.25, to integrate the result prediction error, at least 6 single classifier prediction results are incorrect, the error probability is:Integration result error rate is only 0.034 oh, much smaller than 0.25. The inheritan

meaningless.Thus, further, the following derivation is made:As for why we use the 2 norm here, I understand mainly for the sake of presentation convenience.The meaning of such a big paragraph after each round of algorithm strategy iteration, we require the length of the W to increase the growth rate is capped. (Of course, it is not necessarily the growth of each round, if the middle of the expansion of the equation is relatively large negative, it may also decrease)The above two ppt together to

)/m; at End - End - -%size (J,1) -%size (J,2) - ind3 = A3-Ty; -D2 = (D3 * THETA2 (:,2: End)). *sigmoidgradient (z2); toTheta1_grad = Theta1_grad + d2'*a1/m; +Theta2_grad = Theta2_grad + d3'*a2/m; - the% ------------------------------------------------------------- *jj=0; $ Panax Notoginseng forI=1: Size (Theta1,1) - forj=2: Size (Theta1,2) theJJ = JJ + Theta1 (i,j) *theta1 (i,j) *lambda/(m*2); + End A End theSize (Theta1,1); +Size (Theta1,2); - $ forI=1: Size (THETA2,1) $