convolutional neural network definition

The introduction of convolution neural network Original address : http://blog.csdn.net/hjimce/article/details/47323463 Author : HJIMCE Convolution neural network algorithm is the algorithm of n years ago, in recent years, because the depth learning correlation algorithm for multi-layer

At present, there are neural networks in all aspects of engineering application, and younger brother is now learning neural network, a little conjecture.Most of the current neural network is to adjust their own weights, so as to learn. Under the structure of a certain

, upload to the second cabinet, the machine identified some characteristics of the dog, very vague, continue to upload to the third cabinet, the other part of the dog features identified, the image is gradually clear up, so continue, like "winding" (convolution) action, has been "winding" to the tenth cabinet, the dog's face revealed the "truth", recognition task completed. Ah, it turns out to be the most popular image and speech recognition technology in the world today:

efficiency. The number of neurons that are linearly increased can be expressed in a number of different concepts that increase exponentially.Another advantage of distributed characterization is that the expression of information is not fundamentally compromised, even in the event of a local hardware failure.This idea let Geoffrey Hinton Epiphany, so that he has been in the field of neural network research

)}} {\partial h^{(t)}} \frac{\partial h^{(t)}}{\partial U} = \sum\limits_{t=1}^{\tau}diag (n (h^{(t)}) ^2) \delta^{(t)} (x^{ (t)}) ^t$$In addition to the gradient expression, RNN's inverse propagation algorithm and DNN are not very different, so here is no longer repeated summary.5. RNN SummaryThe general RNN model and forward backward propagation algorithm are summarized. Of course, some of the RNN models will be somewhat different, the natural forward-to-back propagation of the formula will be

information transfer rates (network throughput) Low-cost, small-scale construction of a particular structure network How to add a priori information to a neural network: There is no effective rule to achieve A special process can be implemented: Restricting th

/1406.2661.gan first Paper:lan Goodfellow generative adversarial Networks 5. Algorithm: Using random gradient descent method to train d,g. Specifically also in the above article. 6.DCGAN Principle Introduction: The best model for image processing applications in deep learning is CNN, how CNN and Gan combine. The answer is Dcgan. The principle is the same as Gan. Just replaced the above G and D with two convolutional

the face have moved to another corner of the image, as shown in Fig. 3:The same number of activations occurs in this example, however they occur in a different region of the green and yellow VO Lumes. Therefore, any activation in the first slice of the yellow volume means that a-face is detected, independently of T He face location. Then the fully-connected layer was responsible to ' translate ' a face and a human body. In both examples, an activation is received at one of the fully-connected n

. Theoretical Basis The regression definition of the generalized regression neural network is different from that of the radial basis function to the Gaoshuan value of the least squares superposition, he uses the density function to predict the output. Assuming that x, Y is two random variables, the joint probability density is f (x, y).We get the following formu

applicationsThe blogger made an open source project and collected paper and papers related to the network.Welcome to star and contribution.Https://github.com/zhangqianhui/AdversarialNetsPapersApplication to combat NN. These apps can all be found in my open source project .(1) The paper [2] uses CNN for image generation, where D is used for classification and has a good effect.(2) the thesis [3] uses the prediction of the video frame against NN, which solves the problem that other algorithms can

ICML 2016 's article [Noisy Activation Functions] gives the definition of an activation function: The activation function is a map h:r→r and is almost everywhere.The main function of the activation function in neural network is to provide the nonlinear modeling ability of the network, if not specifically, the activatio

This article is mainly for you to introduce the Python implementation of Neural Network (BP) algorithm and simple application, with a certain reference value, interested in small partners can refer to In this paper, we share the specific code of Python to realize the neural network algorithm and application, for your

This paper, based on the http://en.wikipedia.org/wiki/Backpropagation of Wikipedia, makes a summary of the neural network's back propagation algorithm, and makes a simple formula derivation.A typical post-propagation algorithm for a 3-layer neural network with only 1 hidden layers is as follows:Initialize network weigh

The article does not write clearly please forgive QaqIn this article we will make a very simple image classifier with the CIFAR-10 data set. The CIFAR-10 dataset contains 60,000 images. In this dataset, there are 10 different categories, with 6,000 images in each category. The size of each image is x 32 pixels. While such a small size often poses difficulties in identifying the right category for humans, it is actually a simplification of the computer model and reduces the computational complexi

Foundation of Neural Network (Early Warning: This section begins with mathematical notation and the necessary calculus, linear algebra Operations) Overview of this section As mentioned in the previous lecture, "Learning" is about getting the computer to automatically implement a complex function that completes the mapping from input x to output Y. The basic framework of machine learning is shown in the fol

Keras Introduction?? Keras is an open-source, high-level neural network API written by pure Python that can be based on TensorFlow, Theano, Mxnet, and CNTK. Keras is born to support rapid experimentation and can quickly turn your idea into a result. The Python version for Keras is: Python 2.7-3.6.?? Keras, a Greek-like "horn" (horn), was first released in March 2015 and can run on Windows, Linux, Mac and ot

The development of Googlenet inception V1:The well-designed Inception Module in the Inception V1 improves the utilization of the parameters, Nception V1 removes the final fully connected layer of the model, using the global average pooling layer (which changes the image size to 1x1), in the previous network, The whole connection layer occupies most of the network parameters, it is easy to produce the phenom

An example of image recognition based on convolutional neural network is the preprocessing of input image in common use. Step1:resize STEP2: Go to mean value. It should be noted here that the average is calculated for all training sample images, and then the average is subtracted from each sample picture. The test picture is also subtracted from the mean when i

Let's spit it out. This is based on the Theano Keras how difficult to install, anyway, I am under Windows toss to not, so I installed a dual system. This just feel the powerful Linux system at the beginning, no wonder big companies are using this to do development, sister, who knows ah ....Let's start by introducing the framework: We all know the depth of the neural network, Python started with Theano this

"Proficient in MATLAB neural network" in the book example 10-16, when creating a BP network, the original wording is:　　NET = NEWFF (Minmax (alphabet), [S1 s2],{' Logsig ' Logsig '}, ' Traingdx ');Because there are hints in the process of operation, naturally want to change to a new way of writing (refer to the previous essay "Matlab