neural agility

Here is the [1] derivation of the BP algorithm (backpropagation) steps to tidy up, memo Use. [1] the direct use of the matrix differential notation is deduced, the whole process is very concise. And there is a very big advantage of this matrix form is that it is very convenient to implement the programming Control.But its practical scalar calculation deduction also has certain advantages, for example, can clearly know that a weight is affected by who.Marking Conventions:$L $: The number of layer

Two types of classification: binary Multi-ClassThe following are two types of classification problems (one is binary classification, one is Multi-Class classification)If it is a binary classification classification problem, then the output layer has only one node (1 output unit, SL =1), hθ (x) is a real number,k=1 (K represents the node number in the output layer).Multi-Class Classification (with K categories): hθ (x) is a k-dimensional vector, SL =k, generally k>=3 (because if there are two cl

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 processing, the output of the result is f (x), the F

UFLDL Learning notes and programming Jobs: convolutional neural Network (convolutional neural Networks)UFLDL out a new tutorial, feel better than before, from the basics, the system is clear, but also programming practice.In deep learning high-quality group inside listen to some predecessors said, do not delve into other machine learning algorithms, you can directly to learn DL.So recently began to engage i

Written in front: Thank you @ challons for the review of this article and put forward valuable comments. Let's talk a little bit about the big hot neural network. In recent years, the depth of learning has developed rapidly, feeling has occupied the entire machine learning "half". The major conferences are also occupied by deep learning, leading a wave of trends. The two hottest classes in depth learning are convolution

A feedforward neural network is a artificial neural network wherein connections the the between does not form a units. As such, it is different from recurrent neural networks.The Feedforward neural network was the I and simplest type of artificial neural network devised. [ci

relevant people to have a deeper understanding of the business.Another way of thinking about model work is "complex model + simple features". That is, to weaken the importance of feature engineering and to use complex nonlinear models to learn the relationship between features and to enhance their expressive ability. The deep neural network model is such a non-linear model.is a deep neural network with an

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 details are as follows: Python implements L2 Neural Networks Including the input layer and o

ExplainThis allows us to learn to predict a person ' s identity using a Softmax output unit, where the number of classes equals the Number of persons in the database plus 1 (for the final "not in Database" Class).Reasons for the above options error:1, plus 1 explanation error:Put someone's photo into the convolutional neural network, use the Softmax unit to output the kind, or label, to correspond to these different people, or not any of them, so in S

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 network isThe following is how the error from the pooling layer to the convolution

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 layer-based implementation, each layer corre

A Neural Probabilistic Language Model Neural Probabilistic language model Original thesis Address: Http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf Author: Yoshua BengioRejean DucharmePascal VincentChiristian Jauvin Summary The goal of the statistical language model is to learn the joint probability function of a word sequence in a language, but it becomes difficult because of the problem of di

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 Python. We will share this with you for your reference. The details are as follows: Note: This program is written in Python3. You need to i

Specific principle website: http://wenku.baidu.com/link?url=zSDn1fRKXlfafc_ Tbofxw1mtay0lgth4gwhqs5rl8w2l5i4gf35pmio43cnz3yefrrkgsxgnfmqokggacrylnbgx4czc3vymiryvc4d3df3Self-organizing feature map neural network (self-organizing Feature map. Also called Kohonen Mapping), referred to as the SMO network, is mainly used to solve the problem of pattern recognition class. The SMO network is a unsupervised learning algorithm similar to the previous Kmeans al

End-to-end neural network MT (end-to-end Neural machine translation) is a new method of machine translation emerging in recent years. In this paper, we will briefly introduce the traditional method of statistical machine translation and the application of neural network in machine translation, then introduce the basic coding-decoding framework (Encoder-decoder) i

1 Introduction An XOR operation is a commonly used calculation in a computer: 0 XOR 0 = 0 0 XOR 1 = 1 1 XOR 0 = 1 1 XOR 1 = 0 We can use the code in the first article to calculate this result Http://files.cnblogs.com/gpcuster/ANN1.rar (need to modify the training set), we can find that the results of learning does not satisfy us, because the single layer of neural network learning ability is limited , you need to use more complex networks to lea

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

+ b.tC. C = a.t + bD. C = a.t + b.t9. Please consider the following code: C results? (If you are unsure, run this lookup in Python at any time). AA = Np.random.randn (3, 3= NP.RANDOM.RANDN (3, 1= a*bA. This will trigger the broadcast mechanism, so B is copied three times, becomes (3,3), * represents the matrix corresponding element multiplied, so the size of C will be (3, 3)B. This will trigger the broadcast mechanism, so B is duplicated three times, becomes (3, 3), * represents matrix multipli

/***********************************************************************/ /* 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 Neuron {public : Neuron (); Public: bool Tra

Recently in the study of Artificial neural network (Artificial neural netwroks), make notes, organize ideas Discrete single output perceptron algorithm, the legendary MP Two-valued Network: The value of the independent variable and its function, the value of the vector component only takes 0 and 1 functions, vectors Weight vector: w= (W1,W2,W3.....WN) Input vector: x= (X1,X2,X3.....XN) Training Sample