tensorflow for deep learning from linear regression to reinforcement learning

tensorflow for deep learning from linear regression to reinforcement learning

Alibabacloud.com offers a wide variety of articles about tensorflow for deep learning from linear regression to reinforcement learning, easily find your tensorflow for deep learning from linear regression to reinforcement learning information here online.

-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

Conditions/Prerequisites for regression problems:1) The data collected2) The hypothetical model, a function, which contains unknown parameters, can be estimated by learning the parameters. The model is then used to predict/classify new data.1. Linear regressionAssume that both features and results are linear. That is,

Classification and logistic regression (classification and logistic regression)Http://www.cnblogs.com/czdbest/p/5768467.htmlGeneralized linear model (generalized Linear Models)Http://www.cnblogs.com/czdbest/p/5769326.htmlGenerate Learning Algorithm (generative

logistic regression is a two classification problem, obeys the Bernoulli distribution, the output result is expressed in the form of probability, can write the expression To facilitate the subsequent analysis, we integrate the segmented function For a given training sample, this is what has happened, in the probability of statistics that has happened should be the most probability of the event (the probability of a small event is not easy to happen

Original: http://blog.csdn.net/abcjennifer/article/details/7700772This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression,

}*\ Frac{1}{2}\sum_{i=1}^m (y^{(i)}-\theta^tx^{(i)}) ^2\end{align}\]To make the \ (\ell (\theta) \), you need to minimize\[\frac{1}{2}\sum_{i=1}^m (y^{(i)}-\theta^tx^{(i)}) ^2\]This is the least squares loss function \ (J (\theta) \).4 Local Weighted linear regressionFor a problem from \ (x\in \mathbb{r}\) prediction \ (y\) , in the left figure below, the \ (y=\theta_0+\theta_1x\) is used to match the dataset. In fact, however, the data in the figure

Learning notes TF024: TensorFlow achieves Softmax Regression (Regression) Recognition of handwritten numbersTensorFlow implements Softmax Regression (Regression) to recognize handwritten numbers. MNIST (Mixed National Institute of

1 linear regression algorithmHttp://www.cnblogs.com/wangxin37/p/8297988.htmlThe term regression refers to the fact that we predict an accurate output value based on the previous data, for this example is the price, and there is another most common way to supervise learning, called classification, when we want to predic

The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion;
products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the
content of the page makes you feel confusing, please write us an email, we will handle the problem
within 5 days after receiving your email.

If you find any instances of plagiarism from the community, please send an email to:
info-contact@alibabacloud.com
and provide relevant evidence. A staff member will contact you within 5 working days.