# tensorflow for deep learning from linear regression to reinforcement learning

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### Machine Learning Series-tensorflow-03-linearregressionLinearRegression

: 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

### DeepLearningLearning Note (iii) linearregressionlearning rate optimization Search

-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=

### DeeplearningLearning (b) Matalab operation of linearregression

(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

### Reprint DeepLearning: Two (linearregression practice)

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

### Reprint DeepLearning: Three (multivariance Linearregression practice)

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### The concept of linearregression, logistic regression, various regressionlearning _ machine learning Combat

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

### Deeplearning Exercise 1 linearregression exercises

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

### A classical algorithm for machine learning and Python implementation--linearregression (LinearRegression) algorithm

,..., 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

### The specific explanation of machine Learning Classic algorithm and Python implementation--linearregression (LinearRegression) algorithm

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

### Machine Learning Machines Learning (by Andrew Ng)----Chapter Two univariate linearregression (LinearRegression with one Variable)

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

### Deeplearning exercises multivariable linearregression

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

### The concept learning of linearregression, logistic regression and various 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,

### Linearregressionlearning notes and regressionlearning notes

Linear regression learning notes and regression learning notes Operating System: CentOS7.3.1611 _ x64 Python version: 2.7.5 Sklearn version: 0.18.2 Tensorflow version: 1.2.1 Linear

### Classification and logistic regression (classification and logistic regression), generalized linear models (generalized Linear Models), generating learning algorithms (generative Learning Algorithms)

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

### Machine Learning Algorithm Summary (eight)--Generalized linear model (linearregression, logistic regression)

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

### Stanford Machine Learning---second speaking. multivariable linearregressionLinearRegression with multiple variable

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,

### "CS229 Note one" supervised learning, linearregression, LMS algorithm, normal equation, probabilistic interpretation and local weighted linearregression

}*\ 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 numbers

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

### Machine learning-linearregression algorithm (univariate) LinearRegression with one Variable

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

### Machine Learning-multiple linearregression and machine LinearRegression

Machine Learning-multiple linear regression and machine Linear Regression What is multivariate linear regression? In linear

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