types of machine learning models

identify the cat.Wunda's breakthrough is to make the neural network extremely large, increasing the number of layers and neurons, allowing the system to run large amounts of data and train it. Wunda's project calls images from 10 million YouTube videos, and he really gives deep learning a "depth".Today, in some scenarios, machines trained in deep learning techniques are better at identifying images than hu

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

from:http://blog.jobbole.com/60809/After understanding the machine learning problems that we need to solve, we can think about what data we need to collect and what algorithms we can use. In this article, we'll go through the most popular machine learning algorithms and get a general idea of which methods are available

of neurons is usually activated or suppressed by connections to other neurons. Neuron of the organism: artificial neurons (perception machine): Multilayer perceptron:Neural network representationThe 1993 Alvinn system is a typical example of Ann Learning, which uses a learned Ann to drive a car on the freeway at a normal speed. The input to the Ann is a 30*32 pixel grid with the brightness of the pixel com

types of problems. Some classic popular methods: Perceptron Back-Propagation Tmpnetwork Self-Organizing Map (SOM) Learning vector quantization (LVQ) Deep Learning The deep learning method is an upgraded version of the modern artificial neural network method. It uses rich and inexpensive computing to build large

　In this article we will outline some popular machine learning algorithms.Machine learning algorithms are many, and they have many extensions themselves. Therefore, how to determine the best algorithm to solve a problem is very difficult.Let us first say that based on the learning approach to the classification of the

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.2.2 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: Sensitive to parameter adjustment and kernel function selectio

that employs a scripting language similar to Lisp. In this library, all the statistics-related features you want are available in the R language, including some complex icons. The code in the Machine learning directory in CRAN (which you can think of as a third-party package from a machine brother) is written by a leading figure in the statistical technology app

IntroductionIn real life, we may unknowingly use a variety of machine learning algorithms every day. For example, when you use Google every time, it works well, and one of the important reasons is that a learning algorithm implemented by Google can "learn" how to rank pages. Every time you use a Facebook or Apple photo-processing app, they can automatically ident

discriminant models (discriminative model)The generation method is obtained by the data Learning Joint probability distribution P (x, y) and then the conditional probability distribution P (y| X) as the predictive model, the model is generated : P (Y |X )= P(X,Y)p ( X ) This method is called a build method , which represents the generation relationship of output y produced by a given

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

online: when the user submits the obvious signs, the user's model is updated immediately. The original data streams generated when the user interacts with the application must be saved. In this way, you can re-run the raw stream data required for machine learning for user interest later, and avoid errors during the process of uploading the data due to the fragile cache, as a result, the data is lost. The

Machine learning can be divided into several types according to different computational results. These different purposes determine that machine learning can be divided into different models and classifications in practical applic

stepped on a lot of pits, here and we share a few I think the bigger pit, I hope to be helpful to everyone. I'll introduce a few pits first, and then we'll talk about the feeling and the harvest that we crawled out of the pit.See the model, not the system. If we were to put a name on the pit we had stepped on, the pit must be the first place. Because if you fall into this hole, then the basis for directing your system's direction is probably completely wrong.Specifically, the problem is that wh

This section describes the core of machine learning, the fundamental problem-the feasibility of learning. As we all know about machine learning, the ability to measure whether a machine learni