tensorflow for deep learning from linear regression to reinforcement learning

Learning notes TF024: TensorFlow achieves Softmax Regression (Regression) Recognition of handwritten numbers, tf024softmax TensorFlow implements Softmax Regression (Regression) to recog

found on the internet there are a lot of principles to explain, in fact, this everyone will almost, very few provide code reference, I here Python directly realized, the back will also implement the neural network, regression tree and other types of machine learning algorithmsfirst to a small test sledgehammer, personal expression ability is not very good, we forgive briefly say your own understanding : tra

1-Questions raised 2-Linear regression 3-Theoretical derivation 4-python/spark implementation1 #-*-coding:utf-8-*-2 fromPysparkImportSparkcontext3 4 5theta =[0, 0]6Alpha = 0.0017 8sc = Sparkcontext ('Local')9 Ten deffunc_theta_x (x): One returnSUM ([i * j forI, JinchZip (theta, X)]) A - defCost (x): -thx =func_theta_x (x) the returnThx-x[-1] - - defPartial_theta (x): -DIF =Cost

(Ss_y.inverse_transform (y_test), Ss_y.inverse_transform (lr_y_predict)) $ Print("the mean square error of the linear is:", Lr_mse) -Lr_mae =Mean_absolute_error (Ss_y.inverse_transform (y_test), Ss_y.inverse_transform (lr_y_predict)) - Print("the average absolute error of the linear is:", Lr_mae) - A #evaluation of the SGD model +Sgdr_score =Sgdr.score (x_test, y_test) the Print("the default evaluation va

Tf.variable_scope () and Tf.get_variables () interface. To ensure that each variable has a unique name and can easily modify the number of hidden nodes and the number of network layers, we recommend referencing the code in the project, especially when defining variables to bind Cpu,tensorflow using the GPU by default may cause parameter updates to be too slow. The code above is also common in production environments, whether it's training, implement

Source: https://www.cnblogs.com/jianxinzhou/p/4083921.html1. The problem of overfitting (1) Let's look at the example of predicting house price. We will first perform linear regression on the data, that is, the first graph on the left. If we do this, we can obtain such a straight line that fits the data, but in fact this is not a good model. Let's look at the data. Obviously, as the area of the house increa

Learning notes TF053: Recurrent Neural Network, TensorFlow Model Zoo, reinforcement learning, deep forest, deep learning art, tf053tensorflow Recurrent Neural Networks. Bytes. Natural language processing (NLP) applies the network

TensorFlow is used for simple linear regression and gradient descent examples. tensorflow gradient Linear regression is supervised learning. Therefore, the method and supervised

models on a variety of platforms, from mobile phones to individual cpu/gpu to hundreds of GPU cards distributed systems. From the current documentation, TensorFlow supports the CNN, RNN, and lstm algorithms, which are the most popular deep neural network models currently in Image,speech and NLP. This time Google open source depth learning system

TensorFlow and serving models of the product process. Serving Models in Production with TensorFlow serving: a systematic explanation of how to apply the TensorFlow serving model in a production environment. ML Toolkit: Introduces the use of TensorFlow machine learning libra

application, the learning rate can be adjusted according to the specific situation. There is data to show that at that time, the above algorithm converges. Because it is difficult to calculate efficiently, it is often used instead.3. Logistic regressionThe linear regression model is no longer suitable when the dependent variable can only be evaluated in {0,1}, b

A reprint of the article in the logistic regression there are some basic not mentioned in this article will be explained in detail. So it is recommended to read this one first. This article is reproduced from http://blog.csdn.net/xiazdong/article/details/7950084. ======================================= This article will cover: (1) Definition of linear regression

1. Linear regression (linear regression)： B, multivariate linear regressionMultivariate linear regression: The form is as follows: The order is therefore: there are parameters: Then,

been fitted, you are combining these predictions in a simple way (average, weighted average, logistic regression), and then there is no space for fitting. Unsupervised learning8) Clustering algorithm Clustering algorithm is to process a bunch of data, according to their similarity to the data clustering .Clustering, like regression, is sometimes described as a kind of problem, sometimes describing a c

Machine learning notes (b) univariate linear regression Note: This content resource is from Andrew Ng's machine learning course on Coursera, which pays tribute to Andrew Ng. Model representationHow to solve the problem of house price in note (a), this will be the focus of this article. Now, assuming that

gradient descent algorithm: linear regression Model:　　　　　　　　　　　　　　Linear hypothesis:Squared difference cost function:By substituting each formula, the θ0 and θ1 are respectively biased:By substituting the partial derivative into the gradient descent algorithm, we can realize the process of finding the local optimal solution.The cost function of

), variables (Variable). lesson three TensorFlow linear regression and simple use of classifications. The fourth lesson Softmax, cross-entropy (cross-entropy), dropout, and the introduction of various optimizations in TensorFlow. Fifth Lesson, CNN, and CNN to solve the problem of mnist classification. The sixth lesson

As a fan of machine learning, he has recently been studying with Andrew Ng's machines learning. In the first part of the handout, Ng first explains what is called supervised learning, secondly, the linear model solved by least squares, the logistics regression of the respons

curve to the corresponding point to achieve the purpose of prediction. If the value to be predicted is continuous, such as the above price, then it is a regression problem, if the value to be predicted is discrete, that is, a label,0/1, then it is a classification problem. This learning process is as follows:Second, linear r