udemy machine learning

, 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

Course Address: Https://class.coursera.org/ntumltwo-002/lectureImportant! Important! Important!1. Shallow-layer neural networks and deep learning2. The significance of deep learning, reduce the burden of each layer of network, simplifying complex features. Very effective for complex raw feature learning tasks, such as machine vision, voice.In the following digita

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

Today, Google's robot Alphago won the second game against Li Shishi, and I also entered the stage of the probability map model learning module. Machine learning fascinating and daunting.--Preface1. Learning based on PGMThe topological structure of Ann Networks is often similar. The same set of models are trained in dif

achievements of neuroscientists on visual nerve mechanism, which has a reliable biological basis.Second, convolutional neural networks can automatically learn the corresponding features directly from the original input data, eliminating the feature design process required by the General machine learning algorithm, saving a lot of time, and learning and discoveri

~ ~): Machine learning, data mining (the second half of the main entry): "Introduction to Data Mining" read a few chapters, feel good. Read the review again. "Machine learning" Stanford Open Class is the main. "Linear Algebra", seventh edition, American Steven J.leon There are examples of applications, looking at

change then the iteration can stop or return to ② to continue the loopExample of using the K-mans algorithm on handwritten digital image dataImportNumPy as NPImportMatplotlib.pyplot as PltImportPandas as PD fromSklearn.clusterImportKmeans#use Panda to read training datasets and test data setsDigits_train = Pd.read_csv ('Https://archive.ics.uci.edu/ml/machine-learning-databases/optdigits/optdigits.tra', hea

I find myself coming back to the same few pictures when explaining basic machine learning concepts. Below is a list I find most illuminating.1. Test and Training error: Why lower training error was not always a good thing:esl figure 2.11. Test and training error as a function of model complexity.2. Under and overfitting: PRML figure 1.4. Plots of polynomials has various orders M, shown as red curves, fitted

Recently Learning machine learning, saw Andrew Ng's public class, while studying Dr. Hangyuan Li's "Statistical learning method" in this record.On page 12th There is a question about polynomial fitting. Here, the author gives a direct derivative of the request. Here's a detailed derivation.，In this paper, we first look

Time: 2014.06.26 Location: Base Bytes --------------------------------------------------------------------------------------I. Training error and test error The purpose of machine learning or statistical learning is to make the learned model better able to predict not only known data but also unknown data. Different learning