tensorflow for deep learning from linear regression to reinforcement learning

Learning notes TF062: TensorFlow linear algebra compiling framework XLA, tf062tensorflow XLA (Accelerated Linear Algebra), a specialized Linear Algebra compiler (demain-specific compiler), optimizes TensorFlow computing. Real-time

1 reviews1.1 Supervised learningDefinition: Machine learning algorithm for given correct answerClassification:(1) Regression algorithm: Predicting the output of successive values, such as the prediction of house prices(2) Classification algorithm: The output of discrete values, such as determining whether the disease is a certain type of cancer1.2 Non-supervised learningDefinition: The relationship between

Model Representation NG Video has an example of a house price, a data set between the House area X and the price y: area (x) Price (y) 2104 460 1416 232 1534 315 852 178 ... ... Here is defined: m: Number of training samples, M = 4 visible in the table abovex (i) x^{(i)} : I i input variables/features, in multiple input variables x (i) x^{(i)} represents a set of inputs, such as X (1

doing linear regression, we are concerned about the mean and the standard deviation does not affect the model of learning and parameter θ choice, so here σ set to 1 easy to calculate)2. Three assumptions that form a generalized linear model P (y | x;θ) ∼exponentialfamily (η). The conditional probability distr

ObjectiveThis paper introduces a systematic introduction to the regression part of learning in machine learning, and systematically explains how to use regression theory to predict the continuous value of a classification.Obviously, compared with supervised learning, it has

This paper uses the regularization linear regression model pre-flow (water flowing out of dam) according to the water storage line (water level) of the reservoir, then the Debug Learning Algorithm and discusses the influence of deviation and variance on the linear regression

First, prefaceAs deep learning continues to evolve in areas such as image, language, and ad-click Estimation, many teams are exploring the practice and application of deep learning techniques at the business level. And in the Advertisement Ctr forecast aspect, the new model also emerges endlessly: Wide and

following words:The complete code for this article has been uploaded: Https://gitee.com/beiyan/machine_learning/tree/master/gradientThe random gradient descent (ascent) algorithm is widely used and has a very good effect, and subsequent articles will use the gradient algorithm to solve some problems. No exception, the gradient algorithm is also flawed, such as near the minimum convergence speed, linear search may produce some problems, may be "zigzag

1. Supervised learningRegression algorithms are often used in supervised learning algorithms, so before speaking about regression, the first to say that supervised learning.We have learned a lot of classifier design methods, such as Perceptron, SVM, and so on, their common feature is that according to a given class label samples, training learning machine, and th

1. Model Representation) Our first learning algorithm is linear regression. Let's start with an example. This example is used to predict housing prices. We use a dataset that contains the housing prices in Portland, Oregon. Here, I want to plot my dataset based on the prices sold for different housing sizes: Let's take a look at this DataSet. If one of yo

It should be this time last year, I started to get into the knowledge of machine learning, then the introductory book is "Introduction to data mining." Swallowed read the various well-known classifiers: Decision Tree, naive Bayesian, SVM, neural network, random forest and so on; In addition, more serious review of statistics, learning the linear

Machine learning Notes (iii) multivariable linear regression Note: This content resource is from Andrew Ng's machine learning course on Coursera, which pays tribute to Andrew Ng. One, multiple characteristics (multiple Features)The housing price problem discussed in note (b) only considers a feature of t

This article covers the following topics: Single-Variable linear regression Cost function Gradient Descent Single-Variable linear regressionLooking back at the next section, in the regression problem, we have given the input variable, trying to map to the continuous expected result function to get

A brief introduction of linear regression algorithmlinear regression is a statistical analysis method using regression analysis in mathematical statistics to determine the quantitative relationship between two or more variables, which is widely used. Its expression is y = W ' x+e,e is a normal distribution where the er

calculate the cost function value at this timeEnd% observe the change in cost function value with the number of iterations% plot (J);% observed fitting conditionsStem (x1,y);P2=x*theta;Hold on;Plot (X1,P2);7. Actual UseWhen you actually use linear regression, the input data is optimized first. Includes: 1. Remove redundant and unrelated variables; 2. For nonlinear relationships, polynomial fitting is used

Linear regreesion Linear regression is supervised learning. Therefore, the method and supervised learning should be the same. First, a training set is given and a linear function is learned based on the training set, then, test wh

the following normal distributionThis shows that in the empirical regression model, the estimates of the different XI are unbiased, but the variance size is generally different. The least square method is the unbiased estimator with the smallest variance, that is, the least squares estimation is the best in the whole unbiased model. From the estimate distribution of y0, we can see that if we want to reduce the variance of the model, we should enlarge