machine learning code example

After 2 months of knowledge of machine learning. I've found that machine learning has a variety of directions. Page sort. Speech recognition, image recognition, recommender system, etc. Algorithms are also varied. After seeing the other books, I found that except for the K-mean clustering. Bayesian, neural network, onl

1. What is manifoldManifold Learning Viewpoint: We think that the data we can observe is actually mapped by a low-dimensional pandemic to a high-dimensional space. Due to the limitations of the internal characteristics of the data, some of the data in the higher dimensions produce redundancy on the dimension, which in fact can be represented only by a lower dimension. So intuitively speaking, a manifold is like a D-dimensional space, in a m-dimensiona

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

in fact, Machine Learning has been addressing a variety of important issues. For example , in the mid-decade, people have begun to use neural networks to scan credit card transactions to find fraudulent behavior; at the end of the year,Google Use this technology for Web search. but at that time, machine

of a nonlinear function sigmoid, and the process of solving the parameters can be accomplished by the optimization algorithm. In the optimization algorithm, the gradient ascending algorithm is the most common one, and the gradient ascending algorithm can be simplified to the random gradient ascending algorithm.2.SVM (supported vector machines) Support vectors machine:Advantages : The generalization error rate is low, the calculation cost is small, the result is easy to explain.　　　　cons : Sensit

of the hyperplane is computed as "base", with the average of these points on the two set boundary as the "intercept" of the hyperplane. These points are called support vectors, and the dots are represented by the vector method available. (Image taken from the July algorithm) Enter Data Suppose a training dataset on a given feature space Where, for the first instance (if n>1, that is, X is multidimensional, has multiple attribute characteristics, at this time the vector); The class tag for, wh

say we have some data points, and now we use a straight line to fit these points, so that this line represents the distribution of data points as much as possible, and this fitting process is called regression.In machine learning tasks, the training of classifiers is the process of finding the best fit curve, so the optimization algorithm will be used next. Before implementing the algorithm, summarize some

before, but you need to define T (Y) here:In addition, make:(t (y)) I represents the first element of the vector T (y), such as: (t (1)) 1=1 (T (1)) 2=01{.} is an indicator function, 1{true} = 1, 1{false} = 0(T (y)) i = 1{y = i}Thus, we can introduce the multivariate distribution of the exponential distribution family form:1.2 The goal is to predict the expectation of T (y), because T (y) is a vector, so the resulting output will also be a desired vector, where each element is:Corresponds to th

natural nervous system to dynamically learn from every successful or failed iteration.4. Synaptic. pngSynaptic is a schema-independent (architecture-agnostic), actively maintained node. JS and Browser library that allows developers to build any type of neural network. It has several built-in architectures that allow you to quickly test and compare the similarities and differences between different machine learnin