tensorflow for deep learning from linear regression to reinforcement learning

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

Machine Learning Day No. 0Welcome reprint, please indicate the source (Http://blog.csdn.net/tonyshengtan), respect for labor, respect for knowledge, welcome to discuss.The opening crap.Back to write a blog, although always know that learning is not the end, but still will doubt, learn to what extent can find a job like this (spit groove: The work is too disgusting, the daily task is to sing the praises, whi

http://blog.csdn.net/pipisorry/article/details/43115525Machine learning machines Learning-andrew NG Courses Study notesSingle-Variable linear regression linear regression with one variableModels represent model representationExamp

. Therefore, here we use RSS to calculate the deviation and obtain a system of m + 1 equations, so that we can obtain the equal value, the corresponding linear regression equation is obtained. 2. r regression modeling A built-in data set Swiss is provided in R, which is based on various factors that affect the national economy in Switzerland in 1888. For details

article is not to teach you to train a perfect model with super excellence (this is given in a later document), but just to build a simple model (Softmax regression) to get everyone to taste tensorflow. Although it is a matter of a few lines of code to complete this model, it is important to understand the principles behind the TensorFlow operation and the core

regression. The root number can also be selected based on the actual situation.Regular Equation In addition to Iteration Methods, linear algebra can be used to directly calculate $ \ matrix {\ Theta} $. For example, four groups of property price forecasts: Least Squares $ \ Theta = (\ matrix {x} ^ t \ matrix {x}) ^ {-1} \ matrix {x} ^ t \ matrix {y} $Gradient Descent, advantages and disadvantages of regul

generally divided into regression and classification. If we predict that the value is discrete, then such learning tasks are called classifications, and if the prediction is continuous, such a learning task is called regression. Unsupervised Learning: Clustering is non - s

Data setHouse.csvData overviewCode PackageORG.APACHE.SPARK.EXAMPLES.EXAMPLESFORMLImportOrg.apache.spark.ml.feature.VectorAssemblerImportorg.apache.spark.ml.regression.LinearRegressionImportorg.apache.spark.sql.SparkSessionImportOrg.apache.spark. {sparkconf, sparkcontext}ImportScala.util.Random/*Date: 2018.10.15 description: 7-6 linear regression algorithm forecast price data set: House.csv*/Object

Machine Learning: Linear Regression With Multiple Variables, linearregressionMachine Learning: Linear Regression With Multiple Variables Next, the example of the previous prediction of the house price leads to a multi-variable

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. Model Representation ( Model Creation ) Consider a question: what if we want to predict the price of a house in a given area based on the house price and area data? In fact, this is a linear regression problem. The given data is used as a training sample to train it to get a model that represents the relations

I've been talking about why machines can learn, and starting with this lesson are some basic machine learning algorithms, i.e. how machines learn.This lesson is about linear regression, starting with the minimization of Ein, introducing the Hat Matrix to understand the geometric meaning. Finally, the linear

Logistic regression is used to classify, and linear regression is used to return.Linear regression is the addition of the properties of the sample to the front plus the coefficients. The cost function is the sum of squared errors. Therefore, in the minimization of the cost function, you can directly derivative, so that

use of MATLAB. *.4.gradientdescent.mfunction [Theta, j_history] =gradientdescent (X, y, theta, Alpha, num_iters)%gradientdescent performs gradient descent to learn theta% theta = gradientdescent (X, y, theta, Alpha, num_iters) up Dates theta by% taking num_iters gradient steps with learning rate alpha% Initialize Some useful valuesm= Length (y);%Number of training examplesj_history= Zeros (Num_iters,1); forITER =1: Num_iters% ======================

what is linear regression. The so-called linear regression (taking a single variable as an example) is to give you a bunch of points, and you need to find a straight line from this pile of points. Figure below This screenshot is from Andrew Ng's What you can do when you find this line. Let's say we find A and b that re

: length (theta1_vals) t = [theta0_vals (I); theta1_vals (j)]; j_vals (I, j) = computecost (X, Y, t ); endend (5) figure; % create a graph (6) contour (theta0_vals, theta1_vals, j_vals, logspace (-2, 3, 20); % draw a contour map (7) xlabel ('\ theta_0'); ylabel ('\ theta_1'); if we want to draw the theta0 and theta1 results of linear regression on the contour map, we can: plot (theta (1), theta (2), 'rx ',

') plt.ylabel (' Ratio_sugar ') plt.title (' LDA ') plt.show () W=calulate_w () plot (W)The results are as follows: The corresponding W value is:[ -6.62487509e-04, -9.36728168e-01]Because of the relationship between data distribution, LDA's effect is not obvious. So I changed the number of samples of several label=0, rerun the program to get the result as follows:The result is obvious, the corresponding W value is:[-0.60311161,-0.67601433]Transferred from: http://cache.baiducontent.com/c?m= 9d7

, meaning you have only 10 data, but there are 100 features, obviously, the data is not enough to cover all the features.You can delete some features (keep only data-related features) or use regularization.Exercises1.Don't know how to use both methods at the same time, are these two methods sequential related?Use dividing by the rangeRange = Max-min = 8836-4761 = 4075Vector/range after change to1.94381.27212.16831.1683For the above use mean normalizationAVG = 1.6382Range = 2.1683-1.1683 = 1X2 (4