tensorflow for deep learning from linear regression to reinforcement learning

regression problem. If it is a discrete value, it is a classification problem. Unlike supervised learning,Unsupervised learningDuring training, I did not know the correct results. I went on to give the above example a bunch of fruits to the children, such as apples, oranges, and pears. At the beginning, the children did not know what the fruits were, let the children classify these fruits. After the child

to [0;0]14 Max_itr = 1500;15 Alpha = 0.07;% learning rate for Num_iteratio NS = 1:max_itr18 grad = (1/m). * x ' * ((x * theta)-y);%grd specific is how to calculate the following deduction can be seen, but here the 1/m do not know how to get out, 19 of my is 2m, note that Grad is a Vector of 2*1. And the formula inside the Form 20 is a little different from here, because in the formula XI represents a vector, where x is a matrix, and each row represen

the existence of parameter σ2, the processing process is slightly different, but the results are consistent; the difference between the equation and the derivative of the loss function may be a sign, which is related to the definition of loss function.At this point, the generalized linear model problem is basically solved, but there are still some detail problems left. For example, what are the hypothetical functions hθ (x) mentioned in

of finding the best fitting line is actually looking for the best b b and M M. In order to find the best fit line, here we first define what line is the best line. We define error (cost function): Error function errors (b,m) =1n∑1n ((B+MXI) −yi) 2 error functions \ error_{(b, M)}=\frac{1}{n}\sum_{1}^{n} ((b+mx_i)-y_i) ^{2} The Python code that calculates the loss function is as follows: # y = b + mx def compute_error_for_line_given_points (b, M, points): totalerror = SUM ((((b + M * point[

, the linear change in i-h is to turn y into y-yhat.In another way, Y can be thought of as an ideal f (x) +noise, so, in fact, i-h This linear transformation is applied to the Nosie and becomes y-yhat.What's the point of looking at it? mainly to get the relationship between Ein and noise.Linear regression equivalent to the noise on the original basis to reduce th

ensure reversible ( reversible Sufficient condition : matrix X columns linearly independent )In retrospect, our approach is to use iterative methods to find out the value of the cost function, and not to find the cost function. That is to say, whether the so-called optimal solution can be obtained, either by iteration or by other means, in line with the above conditions.But the reality of the data is not so ideal.If not reversible, how to solve?1, to seek pseudo-inverse (statistics solution )2.

Tags: probability gradient drop RAM log directory UNC measure between playFinishing the Machine Learnig course from Andrew Ng Week1Directory: What is machine learning Supervised learning Non-supervised learning Unary linear regression Model re

estimated:One-element linear regression analysisMultivariate linear regression modelThe core problem of multivariate linear regression: Which variables should be selected?An atypical example (Shiry book p325)RSS (residuals square

Using Python3 to learn the API of linear regressionPrediction of benign and malignant tumors using logistic regression and stochastic parameter estimation regression respectivelyI downloaded the dataset locally and can come to my git to download the source code and dataset:Https://github.com/linyi0604/kaggle1 ImportNumPy as NP2 ImportPandas as PD3 fromSklearn.cr

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