deep learning tutorial andrew ng

Andrew ng Machine Learning course 17 (2)Disclaimer: Reference Please specify source http://blog.csdn.net/lg1259156776/Description: This paper mainly introduces the use of value iteration and policy iteration two kinds of iterative algorithms to solve MDP problem, also introduced in practical application how to accumulate "experience" to update the transfer probab

Model Representation NG Video has an example of a house price, a data set between the House area X and the price y: area (x) Price (y) 2104 460 1416 232 1534 315 852 178 ... ... Here is defined: m: Number of training samples, M = 4 visible in the table abovex (i) x^{(i)} : I i input variables/features, in multiple input variables x (i) x^{

divided by 2 on the basis of the square mean.The equation for judging deviations is called cost Function. The smaller the deviation, the lower the value of the cost function, the better the fit.4. How do I train a model? The purpose of the training model is to achieve good fit, that is to say, the value of cost function is as small as possible.Training here, is to choose a set of coefficients θ (after the model is determined, the parameter of the model is the coefficient theta), to achieve the

Tags: video LSE tun assign DDE INI got the NTSJust finished watching all videos of this course-thank your Andrew for elaborating all basic ML concepts\algorithms in an Easy to understand.I watched most of the course videos on BART, and unfortunately I didn ' t has a chance to work on programming assignments- But again, just following videos helps a ton. All topics is so well organized and internally related. I ' ve got so many ' ah-ha ' moments, and a

function and the derivation of each parameter when using it. we implement the costfunction ourselves and pass in the response parameter. We can return the following two values at a time: For example, call the fminunc () function and use @ to input the pointer to the costfunction function. For the initialized Theta, you can also add options (gradobj = on indicates "Open the gradient target parameter ", that is, we will provide gradient parameters for this function ): 6.7 multi-category classifi

updated, and a final θj value is obtained.The entire derivative is calculated as follows:Vector representation of ④ hypothesis function, cost function and gradient descent algorithmSuppose the vector of the function is represented as follows:The cost function is represented as follows:The vectorization of θ using the gradient descent algorithm is represented as follows:(There is an error in the original formula, the formula after the first equals should not be divided by M, corrected here)The c

for linear regressionWe take the formula of the cost function J into the gradient descent algorithm, then use the concept of partial derivative to simplify the formula, and finally we can get the formula. The specific derivation requires some knowledge of calculus.We can actually use them directly. That is, the algorithm is probably written like this, we use these two formulas to constantly revise the value of two parameters, until the function J reached a minimum value. Now that we have this f

"linear regression, gradient descent"The regular equationThe training features are represented as X-matrices, the results are expressed as Y-vectors, and the linear regression model is still the same, and the loss function is unchanged. Then θ can be derived directly from the following formula:The derivation process involves the knowledge of linear algebra, where the linear algebra knowledge is not expanded in detail.Set m as the number of training samples; x is the independent variable in the

problem of the original problem. Relative to the original problem is only the change of the order of Min and Max, here to take the equal sign. Conditions such as the following descriptive narrations:① If a constrained inequality GI is a convex (convex) function (a linear function belongs to a convex function)② constrained equation hi are affine (affine) functions (Shaped like H (w) =wtx+b)③ and exists W makes for all I,gi (W) In these if, there must be ω?,α?,β, so that Omega is the solution of

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

Omit the use of octave end, later use to see itWeek Three:Logistic Regression:For 0-1 categoriesHypothesis representation:: Sigmoid function or Logistic functionDecision Boundary:Theta's Transpose * small x>=0 is boundaryMay:non-linear decision boundaries, constructing the polynomial of XCost function:Simplified cost function and gradient descent:Because Y has only two values, merging:To find the least biased guide:(The denominator should be ignored)Advanced Optimization:Conjugate gradient,bfgs,

of epsilon items! If the epsilon value is too low, the data after the whitening will appear to be noisy; Conversely, if the epsilon value is too high, the albino data will be too blurry compared to the original data.Epsilon method of selection:A. Draw the eigenvalues of the data graphically; b. Select a characteristic value that is larger than most of the noise in the data to reflect the epsilon .2. How to adjust the epsilon specifically? I don't know, if I had a exercise, I'd be fine.2. When p

Machine Learning-Overview of common matlab programming commands -- Summary from ng-ml-class octave/MATLAB tutorial CourseraA. basic operations and moving data around1 in command line mode, you can use Shift + press enter to append the next line to output 2 length command to apply to the matrix, and return a higher one-dimensional dimension3 help + command is the

, but it does not matter, it is recommended to take a look at this big review every time, each time you will have a different harvest. If you find it hard to understand what others are writing, there are many videos on the web, such as Fudan UniversityProfessor Wulide's "Deep Learning course" Very easy to understand, watching his instructional video will have a better understanding of the many underlying pr

Yii2 deep learning-entry file, yii2 deep learning portal. Yii2's deep learning-entry file. some time before yii2's deep learning portal, I t

UFLDL tutorialfrom ufldl Jump to:navigation, search Description: This tutorial would teach you the main ideas of unsupervised Feature learning and deep learning. By working through it, you'll also get to implement several feature learning/

). The course content is basically code-based programming, there will be a small amount of deep learning theoretical content. The course starts with some of the most basic knowledge from TensorFlow's most basic diagrams (graphs), sessions (session), tensor (tensor), variables (Variable), and gradually talks about the basics of TensorFlow, And the use of CNN and LSTM in TensorFlow. After the course, we will

Reprint: Https://mp.weixin.qq.com/s/J6eo4MRQY7jLo7P-b3nvJg Li Lin compiled from PyimagesearchAuthor Adrian rosebrockQuantum bit Report | Public number Qbitai OpenCV is a 2000 release of the open-source computer vision Library, with object recognition, image segmentation, face recognition, motion recognition and other functions, can be run on Linux, Windows, Android, Mac OS and other operating systems, with lightweight, efficient known, and provides multiple language interfaces. OPENCV's latest

)Ans =01Note: The first data above the main diagonal is taken as the starting data, and is sorted in diagonal order as a column vector form4, V = diag (x) returns the element on the main diagonal of matrix X, similar to Diag (X,K), Case 5 of K=0:V=[1 0 0;0 3 0;0 0 3];Diag (v)Ans =133or instead:V=[1 0 3;2 3 1;4 5 3];Diag (v)Ans =133Note: The data of the main diagonal is taken out as a column vector form5,diag (diag (X))Take the diagonal element of the X-matrix and construct a diagonal matrix with

the loss function (target function) SGD = SGD (l2=0.0,lr=0.05, decay=1e-6, momentum=0.9, nesterov=true) Model.compile ( LosS= ' categorical_crossentropy ', optimizer=sgd,class_mode= "categorical") #调用fit方法, is a training process. The number of epochs trained is set to 10,batch_size of 100. #数据经过随机打乱shuffle =true. Verbose=1, the information that is output during the training process, 0, 1, 23 ways can, does not matter. Show_accuracy=true, each epoch of the training output accuracy. #validation_s