coursera cost machine learning

Each of us in our life or work encountered a variety of optimization problems, such as each enterprise and individual to consider a problem "at a certain cost, how to maximize profits" and so on. The optimization method is a mathematical method, which is a general term for some disciplines that study how to search for certain factors under a given constraint, so as to make certain (or some) indicators reach the optimum. With the deepening of

verified ... The essence is: The gradient descent method only says the direction of descent-the steepest direction, how much each drop is not specifically given. Newton's method or my derivation gives a specific descent, but Newton's method is a variable, that is, the current function value, and my algorithm is a fixed value. Take a look at the second article of reference. The second part and the third part are the introduction of gradient descent method and its improved algorithm: here only t

Tags: 9.png update regular des mini RAC spam ORM ProofOrganize the machine learning course from Adrew Ng week3Directory: Two classification problems Model representation Decision Boundary Loss function Multi-Classification problem Over-fitting problems and regularization What is overfitting How to resolve a fit Regularization method

functions for each gradient descent. Let's draw a value like this:# 画出每一次迭代和损失函数变化theta , Cost_J = gradientDescent(X, y)print(‘theta: ‘,theta.ravel())plt.plot(Cost_J)plt.ylabel(‘Cost J‘)plt.xlabel(‘Iterations‘);The results obtained are theta to[-3.63029144, 1.16636235]So our equation is h=-3.63029144+1.16636235x , we draw a line like this,plt.scatter(X[:,1],y,s=30,c=‘r‘,marker=‘x‘,linewidths=1)plt.xlim(4,24)plt.xlabel(‘Population of City in 10,000s‘)

8 tactics to Combat imbalanced Classes on Your machine learning Datasetby Jason Brownlee on August learning ProcessHave this happened?You is working on your dataset. You create a classification model and get 90% accuracy immediately. "Fantastic" you think. You dive a little deeper and discover this 90% of the data belongs to one class. damn!This is a example of a

. If a real user asks these questions, you can only use "sensitive words" to intercept them. Finally, people must be arranged to build and maintain the query table. As the table grows, the number of people required will also grow, which may make the company's financial department angry. Therefore, querying a table is not a good solution. We need a better solution. Machine Learning refers to the ability of s

select the cost parameter C (http://cran.r-project.org/web/packages/svmpath/index.html) of the support vector machine. The ROCR package provides functions for visualizing the performance of the classifier, such as the ROC Curve (http://cran.r-project.org/web/packages/ROCR/index.html). The caret package provides a variety of functions for establishing predictive models, including parameter selection and imp

Writing programming and writing machine learning modelsBased on the different machine learning models, a large number of characteristic variables are used to predict the fluctuation of the underlying asset price, and the prediction results are evaluated. Machine

equal to the distance between the other two. This red line is the hyperplane that SVM is looking for in two-dimensional situations. It is used for binary classification data. The point supporting the other two online is the so-called support vector. We can see that there is no sample in the middle of the hyperplane and the other two lines. After finding this hyperplane, we use the mathematical representation of the hyperplane data to perform binary classification of the sample data, which is th

easy to fall into the local minimum, and the BP algorithm is "good at" the local minimum. The so-called local minimum, as shown in figure 4, after the network weight is randomly initialized, the gradient is obtained, then, update the parameter with a gradient. If the vertex of the initialization parameter is selected improperly, when the gradient is 0, it may be a point that minimizes the local cost J, rather than the global minimum, naturally, the n

Reprint please indicate source: http://www.cnblogs.com/lighten/p/7593656.html1. PrincipleThis chapter introduces the first algorithm of machine learning--k nearest neighbor algorithm (k Nearest Neighbor), also known as KNN. When it comes to machine learning, it is generally thought to be very complex, very advanced con

, for the supermarket cost table, False reject will sacrifice the cost is relatively large, and false accept sacrifice cost will be small. Therefore, we should try to avoid false reject situation.Examples of CIA fingerprint recognitionIf the CIA, use fingerprint identification to determine if the person has access to the system to view important information.Then,

minimized. The cost function is\ (J (\theta) ={\frac{1}{2}}\sum_{i=1}^m{(H_\theta (x^{(i)})-y^{(i)})The cost function expresses the mean square error of the measured data and. By minimizing the cost function, you can estimate the parameters \ (\theta \). The previous 1/2 is not meaningful, mainly for the sake of the derivation of convenience plus, in fact, 1/m m

Machine Learning (1) gradient descent (gradient descent) Inscription: Recently, I have been studying Andrew Ng's machine learning, so I have taken these notes. Gradient Descent is a linear regression (linear regression). First, we will give a classic example of a house, Area (feet2) Number of rooms Pric

special value of 0, because 0 does not affect the value update of the LR classifier.The partial deletion of sample eigenvalues in training data is a tricky issue, and many documents are devoted to solving the problem, as it is too bad to lose the data directly, and the cost of re-acquisition is expensive. Some optional data loss processing methods include:-Use the mean value of the available features to fill the missing values;-use special values to

From this section is beginning to enter the "normal" machine learning, the reason is "formal" because it began to establish value function (cost function), then optimize the value function to find the weight, and then test the validation. The whole process of machine learning