creating neural network

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convolutional Neural Networks convolutional neural Network (II.)

Transfer from http://blog.csdn.net/zouxy09/article/details/8781543CNNs is the first learning algorithm to truly successfully train a multi-layered network structure. It uses spatial relationships to reduce the number of parameters that need to be learned to improve the training performance of the general Feedforward BP algorithm. In CNN, a small part of the image (local sensing area) as the lowest layer of the input of the hierarchy, the information i

Getting Started with neural network programming

Activationnetwork (iactivationfunction function, int inputscount, params int[] neuronscount): Base (Inputscount, Neuronscount.length)Public virtual double[] Compute (double[] input)Parameter meaning:Inputscount: Number of inputsNeuronscount: Indicates the number of neurons in each layerconstructor function:Public backpropagationlearning (Activationnetwork network)Parameter meaning:Network: The neural net o

Starting from zero depth learning to build a neural network (i) _ Neural network

Artificial intelligence is not mysterious, will be a little subtraction enough. For neurons, when nerves are stimulated, the neurotransmitter is released to the next neuron, and the amount of neurotransmitters released by the next neuron is different for different levels of stimulation, so mimic this process to build a neural network: When entering a data x, simulate input an outside stimulus, after process

dl4nlp--Neural Network (b) Cyclic neural network: BPTT algorithm steps finishing; gradient vanishing and gradient explosion

LSTM unit.for the gradient explosion problem, it is usually a relatively simple strategy, such as Gradient clipping: in one iteration, the sum of the squares of each weighted gradient is greater than a certain threshold, and to avoid the weight matrix being updated too quickly, a scaling factor (the threshold divided by the sum of squares) is obtained, multiplying all the gradients by this factor. Resources:[1] The lecture notes on neural networks a

Realization of heterogeneous or xor__ neural network with simple multilayer neural network

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

Neural network Turing (neural Turing machines, NTM)

Recently, the Google deep Mind team put forward a machine learning model, and a particularly tall on the name: Neural network Turing machine, I translated this article for everyone, translation is not particularly good, some sentences did not read clearly, welcome everyone to criticize Original paper Source: Http://arxiv.org/pdf/1410.5401v1.pdf.All rights reserved, prohibited reprint.

Spiking neural network with pulse neural networks

(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

"Artificial Neural Network Fundamentals" Why do Neural Networks choose "depth"?

Now that the "neural network" and "Deep neural network" are mentioned, there is no difference between the two, the neural network can not be "deep"? Our usual logistic regression can be thought of as a

TensorFlow realization of convolution neural network (Simple) _ Neural network

Code (with detailed comments for source code) and dataset can be downloaded in github: Https://github.com/crazyyanchao/TensorFlow-HelloWorld #-*-Coding:utf-8-*-' convolution neural network test mnist data ' ######## #导入MNIST数据 ######## from Tensorflow.examples.tutorials.mnist Import input_data import TensorFlow as tf mnist = input_data.read_data_sets (' mnist_data/', one_hot=true) # Create default Intera

Data structure of the model: logistic regression, neural network, convolutional neural network

The neural network can be seen in two ways, one is the set of layers, the array of layers, and the other is the set of neurons, which is the graph composed of neuron.In a neuron-based implementation, you need to define two classes of Neuron, WeightAn instance of the neuron class is equivalent to a vertex,weight consisting of a linked list equivalent to an adjacency table and a inverse adjacency table.In the

Self-organizing neural network model and learning algorithm __ Neural network

Self-organizing neural network, also known as self-organizing competitive neural network, is especially suitable for solving the problem of pattern classification and recognition. The network model belongs to the Feedforward neural

From neural network to BP algorithm (pure theory derivation) __ Neural network

The author says: Before having studied once, but after a period of time, many details place already blurred. Recently deduced again, in order to retain as far as possible the derivation idea, specially writes this blog post. On the one hand for their future memories, on the other hand to communicate with you to learn.For this blog post, the following description:1. This blog does not guarantee that the derivation process is completely correct, if there is a problem, please correct me.2. If neces

Machine Learning Public Lesson notes (5): Neural Network (neural network)--Learning

Http://www.cnblogs.com/python27/p/MachineLearningWeek05.html This chapter may be the most unclear chapter of Andrew Ng, why do you say so? This chapter focuses on the back propagation (backpropagration, BP) algorithm, Ng spent half time talking about how to calculate the error item δ, how to calculate the δ matrix, and how to use MATLAB to achieve the post transmission, but the most critical question-why so calculate. The previous calculation of these amounts represents what, Ng basically did n

Neural Network and Deeplearning (3.2) Learning method of improved neural network

gradient descent algorithm to a normalized neural networkThe partial derivative of the normalized loss function is obtained:You can see the paranoid gradient drop. Learning rules do not change:And the weight of learning rules has become:This is the same as normal gradient descent learning rules, which adds a factor to readjust the weight of W. This adjustment is sometimes called weight decay .Then, the normalized learning rule for the weight of the r

(reproduced) convolutional Neural Networks convolutional neural network

convolutional Neural Networks convolutional neural network contents One: Leading back propagation reverse propagation algorithm Network structure Learning Algorithms Two: convolutional neural networks convolutional n

"Artificial Neural Network Fundamentals" Why do Neural Networks choose "depth"?

Now that the "neural network" and "Deep neural network" are mentioned, there is no difference between the two, the neural network can not be "deep"? Our usual logistic regression can be thought of as a

Single-layer perceptron neural network __ Neural network

/***********************************************************************/ /* File: Mc_neuron.h * * 2014-06-04 //////* Description: Single-layer perceptron neural network header file */ /************************************************ / #ifndef _afx_mc_neuron_include_h_ #define _AFX_MC_NEURON_INCLUDE_H_ Class Neuro

Implementation of three kinds of cyclic neural network (RNN) algorithm (from scratch, Theano, Keras) _ Neural network

Preface body RNN from Scratch RNN using Theano RNN using Keras PostScript "From simplicity to complexity, and then to Jane." "Foreword Skip the nonsense and look directly at the text After a period of study, I have a preliminary understanding of the basic principles of RNN and implementation methods, here are listed in three different RNN implementation methods for reference. RNN principle in the Internet can find a lot, I do not say here, say it will not be better than those, here first recomm

Machine Learning Public Lesson Note (4): Neural Network (neural networks)--Indicates

Motive (motivation)For non-linear classification problems, if multiple linear regression is used to classify, it is necessary to construct many high-order items, which leads to too many learning parameters, so the complexity is too high.Neural networks (Neural network)As shown in a simple neural network, each circle re

What is a neural network (depth learning Chapter one)? __ Neural Network

Neural Network Lecture VideoWhat are the neuronts?Storing numbers, returning function values for functionsHow are they connected?a1+ a2+ a3+ A4 +......+ An represents the activation value of the first levelΩ1ω2 ..... Ω7ω8 represents the weight valueCalculates the weighted sum, marks the positive weight value as green, the negative weight value is marked red, the darker the color, the closer the representati

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