neural network stata

P1038 neural network and p1038 Neural NetworkBackground Artificial Neural Network (Artificial Neural Network) is a new computing system with self-learning ability. It is widely used in

This document references: http://www.cnblogs.com/tornadomeet/p/3468450.htmlThank you for that.Generally speaking, the output of a multi-class neural network is generally in softmax form, that is, the activation function of the output layer does not use sigmoid or Tanh functions. Then the output of the last layer of the neural

Python programming simple neural network algorithm example, python Neural Network This example describes the simple neural network algorithm implemented by Python programming. We will share this with you for your reference. The de

The linear neural network is similar to the perceptron, but the activation function of the linear neural network is linear rather than the hard transfer function, so the output of the linear neural network can be any value, and th

"This paper presents a comprehensive overview of the depth of neural network compression methods, mainly divided into parameter pruning and sharing, low rank decomposition, migration/compression convolution filter and knowledge refining, this paper on the performance of each type of methods, related applications, advantages and shortcomings of the original analysis. ” Large-scale

Deep Learning Notes (i): Logistic classificationDeep learning Notes (ii): Simple neural network, back propagation algorithm and implementationDeep Learning Notes (iii): activating functions and loss functionsDeep Learning Notes: A Summary of optimization methods (Bgd,sgd,momentum,adagrad,rmsprop,adam)Deep Learning Notes (iv): The concept, structure and code annotation of cyclic

Example of an artificial neural network algorithm implemented by Python [Based on the back propagation algorithm], python Artificial Neural Network This example describes the artificial neural network algorithm implemented by Pyth

Learning notes TF057: TensorFlow MNIST, convolutional neural network, recurrent neural network, unsupervised learning, tf057tensorflow MNIST convolutional neural network. Https://github.com/nlintz/TensorFlow-Tutorials/blob/master/

First, the main method of neural network performance tuning the technique of data augmented image preprocessing network initialization training The selection of activation function different regularization methods from the perspective of data integration of multiple depth networks 1. Data augmentation The generalization ability of the model can be improved by inc

In the Perceptron neural network model and the linear Neural network model learning algorithm, the difference between the ideal output and the actual output is used to estimate the neuron connection weight error. It is a difficult problem to estimate the error of hidden layer neurons in

I've been watching "neural network Design_hagan" Then you want to implement an XOR network yourself. Because the single layer neural network can not divide the different or the judgment to two kinds. According to a^b= (a~b) | (~AB) And I tried it. Or and with both ca

(Original address: Wikipedia)Introduction:Pulse Neural Network spiking Neuralnetworks (Snns) is the third generation neural network model, the simulation neuron is closer to reality, besides, the influence of time information is considered. The idea is that neurons in a dynamic neu