tensorflow for deep learning from linear regression to reinforcement learning

: 0300 cost = 0.134895071 W = 0.3842099 B =-0.16695316EPOCH: 0350 cost = 0.128200993 W = 0.37620357 B =-0.10935676EPOCH: 0400 cost = 0.122280121 W = 0.36867347 B =-0.055185713EPOCH: 0450 cost = 0.117043234 W = 0.36159125 B =-0.004236537EPOCH: 0500 cost = 0.112411365 W = 0.3549302 B = 0.04368245EPOCH: 0550 cost = 0.108314596 W = 0.34866524 B = 0.08875148EPOCH: 0600 cost = 0.104691163 W = 0.34277305 B = 0.13114017EPOCH: 0650 cost = 0.101486407 W = 0.33723122 B = 0.17100765EPOCH: 0700 cost = 0.0986

-alpha (alpha_i). *grad; End Plot (0: the, Jtheta (1: -),Char(Plotstyle (alpha_i)),'linewidth',2)%It is important to use the CHAR function to convert the packet () to the cell after the package () index.%so you can use the Char function or the {} index, so you don't have to convert. %a learning rate corresponding to the image drawn out later to draw the next learning rate corresponding to the image. onif(1=

(theta0_vals, theta1_vals, j_vals)%draw an image of the parameter and the loss function. Pay attention to using this surf to compare the egg ache, surf (x, y, z) is this,Wuyi%x,y is a vector, Z is a matrix, a mesh made of X, Y ( -*100 points) with each point of Z the% to form a graph, but how does it correspond, where the egg hurts is that the second element of your x and the first element of y are formed by the point Not and Z (2，1) value corresponds!! -% but and Z (1,2) corresponding!! Becau

surf, orElseThe axes'll be flippedj_vals= J_vals ';%Surface Plotfigure;surf (theta0_vals, Theta1_vals, j_vals) Xlabel (' \theta_0 '); Ylabel (' \theta_1 ');%Contour plotfigure;% Plot j_vals as contours spaced logarithmically between 0.01 and 100Contour (theta0_vals, Theta1_vals, J_vals, Logspace (-2, 2, 15))%Draw the Contour line Xlabel (' \theta_0 '); Ylabel (' \theta_1 ');% is similar to an escape character, but can only be a parameter 0~9Resources:http://openclassroom.stanford.edu/MainFolder

Objective: This article is mainly to practice multivariable linear regression problem (in fact, this article also on 3 variables), reference page: http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course= Deeplearningdoc=exercises/ex3/ex3.html. In fact, in the previous blog Deep learning: Two (

Regression is to try to find out the number of variables in the relationship between the change in the expression of the function expression, this expression called the regression equation. Conditions/Prerequisites for regression issues: 1) collected data 2 The hypothetical model The model is a function that contains unknown parameters and can be estimated by

Linear regression ExercisesFollow Andrew Ng and do the exercises: http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=DeepLearningdoc= Exercises/ex2/ex2.htmlThis section does a little exercise in linear regression, with data from the Web site above, where X is the height of the little boy,Y is the age

,..., partθn . So, how do you find theta when there are X and y in your hand? In the regression equation, the method of finding the best regression coefficients corresponding to the characteristics is the sum of the squares of minimizing errors. The error here is to predict the difference between the Y value and the true Y value, and using the simple summation of the error will make th

regression coefficient θ=θ0, θ1 ,..., partθn . So, how can you find theta if you have x and y in your hand? In the regression equation, the method to obtain the corresponding optimal regression coefficients is to minimize the sum of squares of errors.The error here refers to the difference between the predicted Y value and the tru

Chapter Two univariate linear regression (Linear Regression with one Variable) 1.Model RepresentationIf we return to the problem of training set (Training set) as shown in the following table:The tag we will use to describe this regression problem is as follows :M represent

multivariable linear regression ( multivariate Linear Regression)Jobs from Links: http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=DeepLearningdoc=exercises/ex3 /ex3.htmlThis time, the multivariate linear regression