tensorflow for deep learning from linear regression to reinforcement learning

, although also known as Multilayer perceptron (multi-layer Perceptron), is actually a shallow layer model with only one layer of hidden layer nodes. In the the 1990s, a variety of shallow machine learning models were presented, such as support vector machines (svm,support vector machines), boosting, and maximum entropy methods (such as Lr,logistic Regression). The structure of these models can basically be

Usually, we use deep learning to classify, but sometimes it is used to do regression. Original source: Regression Tutorial with the Keras Deep Learning Library in Python 1. Here the author uses keras and Python's Scikit-learn ma

variance score = 0.34R2 score = 0.33Now we will create a 10-order polynomial and train the regression. and provide sample data points.polynomial = polynomialfeatures (degree = ten== [2.23, 1.35, 1.12== linear_ Model. Linearregression () Poly_linear_model.fit (x_train_transformed, y_train)print("\nlinear regression:\n", Reg_linear_mul.predict (datapoint))print("\ Npolynomial

layer of the neural network can be used as a linear classifier, and then we can replace it with a classifier with better performance. During the study, we can find that adding the features obtained by automatic learning to the original features can greatly improve the accuracy, and even make the classification problem better than the current best classification algorithm! There are some variants of autoenc

sample belongs to the class.However, because a sample data usually has multiple characteristics, we can not directly into the logistic regression formula, so we need to use the linear regression described earlier, so that the sample's multiple eigenvalues to generate a specific value, in the formula into the equation, so the expression of z is as follows:　　　　You

seldom use the PLA, linear SVR because it is less effective than the other three linear models. and Kernel ridge regression and kernel logistic regression is also not commonly used, because its coefficients are mostly not 0, so in the forecast time will cost a lot of meaningless calculation.reprint Please indicate the

implementationStream_executor # stream processingTensorboard # App, Web Support, and script supportTensorflow. bzlTf_exported_symbols.ldsTf_version_script.ldsTools # miscellaneous toolsUser_opsWorkspace. bzl Contirb directory. Save common functions and encapsulate advanced APIs. Not officially supported. After the advanced API is complete, it is officially migrated to or removed from the core TensorFlow directory. Some packages have a more complete i

category by two, and get N classifiers.When testing is required, input the data into each classifier, selecting one of the largest probabilities as the output.SummaryLogistic regression is built on the basis of linear regression. The model is: the probability that the output is 1 through the sigmoid function. The application should conform to the Bernoulli distr

post-pruning algorithm (its disadvantage is that it is computationally large), with the minimum expected cost of miscalculation (ECM) and the minimum description length (DML) algorithm. A post-pruning algorithm is described below, which determines whether to merge leaf nodes based on the test data and the error size:Split the test data for the given tree:If The Eithersplit is a tree:call prune on that splitCalculate theerror associated with merging leaf nodesCalculate Theerror without mergingIf

distributed computing of heterogeneous devices, which can automate 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. Open source meaning this time Google Open sou

Moving DL we have six months of time, accumulated a certain experience, experiments, also DL has some of their own ideas and understanding. Have wanted to expand and deepen the DL related aspects of some knowledge.Then saw an MIT press related to the publication DL book http://www.iro.umontreal.ca/~bengioy/dlbook/, so you have to read this book and then make some notes to save some knowledge of the idea. This series of blog will be note-type, what is bad to write about the vast number of Bo frie

, according to the biased formula:y=lnx y'=1/x. The second step is to attribute G ' (z) = g (z) (1-g (z)) according to the derivation of G (Z). The third step is the normal transformation. So we get the update direction of each iteration of the gradient rise, then the iteration of Theta represents the formula: This expression looks exactly the same as the LMS algorithm's expression, but the gradient rise is two different algorithms than the LMS, because it represents a nonlinear function. Two

Last year in Beijing participated in a big data conference organized by O ' Reilly and Cloudera, Strata , and was fortunate to have the O ' Reilly published hands-on machine learning with Scikit-learn and TensorFlow English book, in general, this is a good technical book, a lot of people are also recommending this book. The author of the book passes specific examples, Few theories and two mature Python fra

LR implementation method. 1. Linear return Linear regression is a simpler algorithm in machine learning (ML), and we focus on the simple mathematical ideas and intuitive explanations behind them, followed by mathematical deduction. Linear

This article will use an example to tell how to use Scikit-learn and pandas to learn ridge regression.1. Loss function of Ridge regressionIn my other article on linear regression, I made some introductions to ridge regression and when it was appropriate to use ridge regression