machine learning sample code

framework (orch), and Julia does not exist. Which language is the most popular programming language? The answer should be clear. Python, Java, and R are the most popular skills when it comes to machine learning and data science. If you want to focus on deep learning instead of general machine

Original address Mathematics is the foundation of computer technology, linear algebra is the basis of machine learning and deep learning, the best way to understand the knowledge of the data I think is to understand the concept, mathematics is not only used for exams in school, but also the essential basic knowledge of the work, in fact, there are many interestin

belief Networks (DBN), convolutional networks (convolutional network), Stack-type Automatic encoder (stacked auto-encoders).2.12 Reducing the dimension of the algorithmLike the clustering algorithm, the reduced dimension algorithm tries to analyze the intrinsic structure of the data, but the reduced dimension algorithm attempts to use less information to summarize or interpret the data in an unsupervised learning way. Such algorithms can be used to v

support vector machine, and when the training data is approximately linearly separable, it is maximized by the soft interval (soft margin Maximization), also learn a linear classifier, linear support vector machine, also known as soft interval support vector machine, when the training data linear non-tick, through the use of nuclear techniques (kernel trick) and

is still published as a reading note, not involving too many code and tools, as an understanding of the article to introduce machine learning.The article is divided into two parts, machine learning Overview and Scikit-learn Brief Introduction, the two parts of close relationship, combined writing, so that the overall l

The topic of machine learning techniques under this column (machine learning) is a personal learning experience and notes on the Machine Learning Techniques (2015) of Coursera public co

Types of learning according to my personal understanding, the classification of learning methods in machine learning helps us face a specific problem, you can select an appropriate machine learning algorithm based on your goals. F

July Algorithm-December machine Learning online Class -12th lesson note-Support vector machine (SVM) July algorithm (julyedu.com) December machine Learning Online class study note http://www.julyedu.com?What to review: Duality problem KKT conditions? SVM1.1

Support vector machine algorithm in deep learning does not fire up 2012 years ago, in machine learning algorithm is a dominant position, the idea is in the two classification or multi-classification tasks, the category of the super-plane can be divided into many kinds, then which kind of classification effect is the be

,m)) return jdef clipAlpha(aj,H,L): if aj > H: aj = H if L > aj: aj = L return ajdef smoSimple(dataMatIn, classLabels, C, toler, maxIter): dataMatrix = mat(dataMatIn); labelMat = mat(classLabels).transpose() b = 0; m,n = shape(dataMatrix) alphas = mat(zeros((m,1))) iter = 0 while (iter The running result is shown in figure 8: (Figure 8) If you are interested in the above code, you can read it. If you use it

1. Scikit-learn IntroductionScikit-learn is an open-source machine learning module for Python, built on numpy,scipy and matplotlib modules. It is worth mentioning that Scikit-learn was first launched by David Cournapeau in 2007, a Google Summer of code project, since then the project has been a lot of contributors, And the project has been maintained by a team of

: Since the predicted data is close to many of the class A training samples, and the other classes are not very similar, then the prediction data can be judged as a class.In the above description, the use of Euclidean distance as the predicted data and training samples of the comparison results, that is, assuming the test sample is a , and the first xi sample in the training

comprehensive toolkit ever seen on RubyRuby Linguistics-This framework can be used to build linguistic tools for Ruby objects in any language; it includes a universal front-end language-agnostic, a module that maps language code to language names, and a module with many English language toolsStemmer-Make Ruby available in the Libstemmer_c interfaceRuby Wordnet-wordnet's Ruby Interface LibraryRaspel-aspell interface bound to RubyUEA Stemmer-uealite St

neighbor point, and then can establish a neighbor map, so calculate the distance between two points of the problem, The transition becomes the shortest path problem (Dijkstra algorithm) between two points on the nearest neighbor graph.So what is the ISOMAP algorithm? In fact, it is a variant of the MDS algorithm, the same idea as the MDS, but in the calculation of the distance of the high-dimensional space is the geodesic distance, rather than the real expression of the European distance betwee

one, factor decomposition machineFMthe Modelfactor decomposition Machine (factorization machine, FM) is bySteffen Rendlea machine learning algorithm based on matrix decomposition is proposed. 1, Factor decomposition machineFMThe advantagesfor factor decomposition machinesFM, the most important feature is that the spars

assumptions tend to be 0, but the actual labels are 1, both of which indicate a miscarriage of judgment. Otherwise, we define the error value as 0, at which point the value is assumed to correctly classify the sample Y.Then, we can use the error rate errors to define the test error, that is, 1/mtest times the error rate errors of H (i) (xtest) and Y (i) (sum from I=1 to Mtest).Stanford University public Class mac