tensorflow for deep learning from linear regression to reinforcement learning

an open source software library that uses a data flow graph (stream graphs) for numerical computations. A node (Nodes) represents a mathematical operation in a graph, and a line (edges) in a graph represents an array of multidimensional data, the tensor (tensor), that is interconnected between nodes. Its flexible architecture allows you to expand computing on a variety of platforms, such as one or more CPUs (or GPU), servers, mobile devices, and so on in a desktop computer.

. Decision-making boundaries (decision Bound)The function $g (z) $ is a monotone function, $h _\theta (x) \geq 0.5$ Predictive output $y=1$, equivalent to $\THETA^TX \geq 0$ predictive output $y=1$; $\theta (x) This does not require specific to the sigmoid function, only need to solve $\THETA^TX \geq 0$ that can get the corresponding classification boundary. Examples of linear classification boundary and nonlinear classification bou

From this section is beginning to enter the "normal" machine learning, the reason is "formal" because it began to establish value function (cost function), then optimize the value function to find the weight, and then test the validation. The whole process of machine learning must be through the link. The topic to study today is logistic regression, and logistic

A virtual variable (dummy variables), also known as a virtual variable, a nominal variable, or a dummy variable, is a manual variable used to reflect a qualitative attribute. It is a quantified independent variable, usually with a value of 0 or 1. The introduction of dummy variables can make linear regression models more complex, but the problem description is more concise. An equation can act as two equati

Machine Learning (4) Logistic Regression 1. algorithm Derivation Unlike gradient descent, logistic regression is a type of classification problem, while the former is a regression problem. In regression, Y is a continuous variable, while in classification, Y is a discrete gr

Resources A Tour of the machine learningClassifers Using Scikit-learn IntroductionWhen we classify, the eigenvalues in the sample are generally distributed in the real number field, but what we want is often a similar probability value in [0,1]. Or so, in order for the eigenvalues not to cause interference between the differences between the large, for example, only one feature value is particularly large, but the other values are very small, we need to normalization of the data. T

rate, the higher the accuracy. Mini-batch size. The size of each batch determines the weight update rules. The average value is obtained and the weight is updated only after the entire batch of sample gradients are calculated. The higher the batch, the faster the training speed. The matrix and linear algebra libraries are used for acceleration, and the weight update frequency is low. The smaller the batch, the slower the training speed. Set the machi

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UFLDL Learning notes and programming Jobs: Softmax Regression (Softmax regression)UFLDL out a new tutorial. Feel better than before, starting from the basics. The system is clear and has programming practice.In deep learning high-quality group inside listen to some predecess

Logistic regression algorithm debugging First, the principle of the algorithmLogistic regression algorithm is an optimization algorithm, which is mainly used for classification problems with only two kinds of labels. The principle is to use a straight line to fit some data points and divide the data set. Broadly speaking, this is also a multivariate linear

and in application. In contrast, because of the difficulty of theoretical analysis, training methods need a lot of experience and skills, this period of shallow artificial neural network is relatively quiet.Deep learning is the second wave of machine learning.In 2006, Professor Geoffrey Hinton of the University of Toronto in Canada and his student Ruslansalakhutdinov published an article in science that opened the wave of

Original address Mathematics is the foundation of computer technology, linear algebra is the basis of machine learning and deep learning, the best way to understand the knowledge of the data I think is to understand the concept, mathematics is not only used for exams in school, but also the essential basic knowledge of

from:http://blog.csdn.net/lsldd/article/details/41551797In this series of articles, it is mentioned that the use of Python to start machine learning (3: Data fitting and generalized linear regression) refers to the regression algorithm for numerical prediction. The logistic regress