parameter sweep machine learning

7th Chapter Support Vector MachineSupport Vector Machine (SVM) is a two-class classification model of machines. Its basic model is a linear classifier that defines the largest interval in the feature space, and the support vector machine also includes the kernel technique, which makes it a substantial nonlinear classifier. The learning strategy of support vector

through data and models. However, in the classroom, Mr. Yu proposed that these three elements are not important. The most important thing is the demand. Once there is a need, various methods will be used to solve the problem. He is Baidu's Deputy Technical Director. In addition, the main application scenarios of machine learning include computer vision, speech recognition, natural speech processing, search

gradient descent method is used to optimize the objective function, the objective function is derivative, and the gradient change caused by the regularization term is 1 when the wj>0 wj>0 is taken 1. The resulting parameter WJ WJ minus the product of the learning rate and the (13) type, so when the WJ WJ is greater than 0, WJ WJ subtracts a positive number, causing WJ WJ to decrease, and when WJ WJ is less

be able to find the global optimal solution.When the training sample is very large, each update parameter needs to traverse all the sample calculation total error, so that the learning speed is too slow; this time the random gradient descent algorithm that calculates the error update parameters of a sample is usually more thanThe batch gradient descent method is faster. (Theoretically, there is no guarante

memory;Svm:To learn how to use LIBSVM and some parameter tuning experience, it is also necessary to understand some of the ideas of the SVM algorithm:1. The optimal classification surface in SVM is the maximum geometric margin for all samples (why choose the maximum interval classifier, from a mathematical point of view?). NetEase Deep Learning Post interview process has been asked. The answer is that ther

converge or even diverge. .One thing worth noting:As we approach the local minimum, the guide values will automatically become smaller, so the gradient drop will automatically take a smaller amplitude, which is the practice of gradient descent. So there's actually no need to reduce the alpha in addition, we need a fixed (constant) learning rate α. 4. Gradient Descent linear regression (Gradient descent for Linear Regression) This is the method of us

Python is widely used in scientific computing: Computer vision, artificial intelligence, mathematics, astronomy, etc. It also applies to machine learning. This article lists and describes Python's wide application in Scientific Computing: Computer vision, artificial intelligence, mathematics, astronomy, etc. It also applies to machine

search path in Classpath, and if neither-classpath nor Classpath are set, the virtual machine uses the current path (.). As a class search path.It is recommended to use-classpath to define the classpath of a virtual confidential search, instead of using the search path of the environment variable CLASSPATH to reduce the potential conflicts that exist when multiple projects are using classpath at the same time. For example, apply 1 to use Class G in A

architecture. The local connection enables the network to extract the local characteristics of the data, the weight sharing greatly reduces the difficulty of the network training, one filter extracts only one feature, the whole picture (or the voice/text) of the convolution; the pooling operation, together with the multi-level structure, realizes the dimensionality reduction of the data, The low-level local features are combined into higher level features to represent the whole picture. 5 What

PrefaceThe Machine learning section records Some of the notes I have learned in the process of learning, including the online course or tutorial's study notes, the reading notes of the papers, the debugging of algorithmic code, the thinking of cutting-edge theory and so on, which will open different column series for different content.Machine

size of the model, and thus increasing the numbers of machines, but the traffic on the network does not affect acceleration by the graph.Scaling with more replicasThe model size is constant, but the number of copies of the parameter is increased, that is, the parallelization of the data becomes larger. Look at the acceleration situation.PerformanceThe effect is as follows, lifting greatly drops. As the model becomes larger, the effect becomes better.

We will now start training the model and enter the parameters as follows:The number of factors in the rank:als, usually the larger the better, but has a direct impact on memory usage, usually rank between 10 and 200.Iterations: The number of iterations, each iteration reduces the reconstruction error of the ALS. After several iterations, the ALS model converges to get a good result, so many iterations (usually 10 times) are not required in most cases.Lambda: The regularization

: Random initialization Loop until convergence { Each State transfer count in the sample is used to update and R Use the estimated parameters to update V (using the value iteration method of the previous section) According to the updated V to re-draw } In step (b) We are going to do a value update, which is also a loop iteration, in the previous section we solved v by initializing v t

] = \displaystyle{\sum_{m=0}}mbin (m| N,\MU) =n\mu\)\ (Var[m] = \displaystyle{\sum_{m=0}} (M-\mathbb{e}[m]) ^{2}bin (m| N,\MU) =n\mu (1-\MU) \) Beta distribution (distribution) This section considers how to introduce a priori information into a binary distribution and introduce a conjugate priori (conjugacy prior)Beta distribution is introduced as a priori probability distribution, which is controlled by two hyper-parameters \ (A, b\). \ (Beta (\mu|a,b) =\frac{\gamma

parameter estimation, some are not able to solve the most solvable optimization problems, the conversion to the probability distribution of the estimation problem, through the probabilistic Inference to solve--such as using Gibbs sampling to train latent Dirichlet allocation model. Whether it is numerical optimization or sampling, it is the process of iterative optimization: Do two things every step of the iteration. The first is the evaluation o

Reprinted article: Norm Rule in machine learning (i) L0, L1 and L2 norm[Email protected]Http://blog.csdn.net/zouxy09Today we talk about the very frequent problems in machine learning: overfitting and regulation. Let's begin by simply understanding the L0, L1, L2, and kernel norm rules that are commonly used. Finally, w

normalized disposal, each dimension of the data are converted to 0, 1 interval, thereby reducing the number of iterations, improve the convergence rate of the algorithm.4. Selection of K valuesAs mentioned earlier, the number of clusters in K-means clustering K is a user-defined parameter, then how can users know if K is the correct choice? How do you know if the generated clusters are better? Like the K-value determination method of K-nearest neighb

We are now starting to train the model, and also enter a number of parameters such as the following:The number of factors in the rank:als. Generally, the bigger the better, but has a direct impact on memory usage, usually rank between 10 and 200.Iterations: The number of iterations, each iteration will reduce the reconstruction error of the ALS. After several iterations, the ALS model will converge to get a good result, so many iterations (typically 10 times) are not required in most cases.Lambd

changed or modified as required.Other faster feature selection methods include: Select the best feature from a model. We can observe the sparse of a logical model, or train a random forest to select the best features and then use them on other machine learning models.　　Remember to keep a small number of estimator and minimize the parameters so that you don't over-fit.The selection of features can also be a

=sigmoid (Z2); A2=[ones (1,size (a2,2)); A2]; Z3=THETA2*A2; A3=sigmoid (Z3); Delta_3=a3-y_vec; Gz2=[0;sigmoidgradient (z2)]; Delta_2=theta2 ' *delta_3.*gz2; Delta_2=delta_2 (2:end); Delta2=delta2+delta_3*a2 '; Delta1=delta1+delta_2*a1 '; endtheta1_grad=1/m*delta1; THETA2_GRAD=1/M*DELTA2; Theta1 (:, 1) = 0; Theta1_grad=theta1_grad+lambda/m*theta1; THETA2 (:, 1) = 0; theta2_grad=theta2_grad+lambda/m*theta2;%-------------------------------------------------------------% ====