coursera cost machine learning

1, using machine learning to solve the problem, we use mathematical language to describe it, and then build a model, such as regression model or classification model to describe the problem; 2, by minimizing the error, maximum likelihood, maximum posterior probability and so on to establish the cost function of the model, to transform to the optimization probl

Machine learning (a) gradient descent algorithmBecause the algorithm is best applied to practical problems to make the reader feel its true usefulness, let me first describe a practical problem (gradient descent algorithm to help solve the problem): given a specified set of data, such as the housing area and the housing price of a number of data pairs (area, Price) composition (Wunda Teacher's course is the

category by two, and get N classifiers.When testing is required, input the data into each classifier, selecting one of the largest probabilities as the output.SummaryLogistic regression is built on the basis of linear regression. The model is: the probability that the output is 1 through the sigmoid function. The application should conform to the Bernoulli distribution in the output.The gradient descent algorithm is also useful, and there are some more efficient algorithms. At first, you can us

Machine learning-2-linear regressionFirst of all, our teacher really sucks in class. It's really rotten.PPT also only to meaningless formula, but also do not explain what is doing.Regression What is regressionFirst, regression is a kind of supervised learning , regression problem, try to predict the continuous output, and try to predict the discrete output of

rise, below gradient descent) in real-world problems can be problematic,Here is the gradient descent algorithm, which is also used in the linear regression, the final optimization equation is the same as the above logical regression. The iteration formula is as follows:Every time you adjust to the direction of the W, you get the bias of the W, then you initialize a W, and the next iteration is fine. In addition, we note that the biasing of W is a summation of all the data points, so in each ite

Machine Learning:neural NetworkA: PrefaceDefinition of the neural network on 1,wikipedia:InchMachine Learning, Artificial neural networks (anns) is a family of statistical learning algorithms inspired byBiological Neural Networks(TheCentral Nervous Systemsof animals, in particular theBrain) and is used to estimate orapproximatefunctionsThat can depend on a large

samples from n samples that have been put back2. Set up a classifier on the full attribute of the N samples (cart,svm)3, repeat the above steps, the establishment of a m classifier4, the prediction of the use of voting methods to obtain resultsBoostingBoosting in training will give a weight to the sample, and then make the loss function as far as possible to consider those Sub-error class samples (such as to the sub-class of the weight of the sample to increase the Value)Convex optimizationThe

1. Alternating Least SquareALS (Alternating Least Square), alternating least squares. In machine learning, a collaborative recommendation algorithm using least squares method is specified. As shown, u represents the user, v denotes the product, the user scores the item, but not every user will rate each item. For example, user U6 did not give the product V3 scoring, we need to infer that this is the task of

Boring, adapt to the trend, learn the Python machine learning it.Buy a book, first analyze the catalogue it.1. The first chapter is the Python machine learning ecosystem.1.1. Data science or machine learning workflow.It is then di

normal equations omit the step of feature scaling when dealing with multivariable regression equations, simply follow the steps of a single variable and be more concise.Three, the choice of learning rateThe efficiency of gradient descent is greatly influenced by the learning rate, which is too small, the convergence rate is very slow, and the number of iterations is increased; when too large, each iteratio

NG Machine Learning Video notes (ii)--Gradient descent algorithm interpretation and solving θ (Reproduced please attach this article link--linhxx) First, the interpretation gradient algorithmA gradient algorithm formula and a simplified cost function diagram, as shown in.1) Partial derivativeBy the know, at point A, its partial derivative is less than 0, so θ min

Machine learning is the process of selecting the optimal model in the model space, the so-called optimal model, and can well fit the existing data set, and correctly predict the unknown data.So how to evaluate the pros and cons of a model, using the cost function to measure the degree of error prediction. There are many cost