convolutional neural network theory

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

It took a week to learn about neural networks after soy sauce in the Knowledge Engineering Center. The teacher arranged a question and asked me to try it. I did a little simple. I conducted several groups of tests and wrote a summary report. I posted it here. After more than a week of experimentation, I have a simple understanding of this issue. The following is my thoughts on this issue. In the last two days, I suddenly felt that the problem was much

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

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

/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

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

Code address for this section Https://github.com/vic-w/torch-practice/tree/master/rnn-timer RNN full name Recurrent neural network (convolutional neural Networks), which is a memory function by adding loops to the network. The natural language processing, image recognit

BP networks.)② uses the method of probability theory-maximum likelihood estimation to design the error objective function.The mathematical principle of LMS (minimum mean square) used in linear neural networks can be obtained by the maximum likelihood estimation + hypothesis error probability model. (see Andrew Ng Video)The likelihood function's numerical range is larger than the LMS, especially in two case

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

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

Python implementation of multilayer neural networks. The code is pasted first, the programming thing is not explained. Basic theory reference Next: Deep Learning Learning Notes (iii): Derivation of neural network reverse propagation algorithm Supervisedlearningmodel, Nnlayer, and softmaxregression that appear in your c

This article is reproduced from: Http://www.cnblogs.com/lillylin/p/6204099.html xiangbai--"AAAI2017" textboxes:a Fast Text detector with A/single Deep neural network Catalog Authors and related link methods summarize innovation points and contribution methods summary of experimental results and harvesting points author and related link author Thesis downloads Lio Minghui, Shi, Baixiang, Wang Xinggang L

. When the center vector and radius of each hidden layer are determined, the unknown parameters in the network structure are only the linear weights and thresholds of the output cells. In the adjustment of linear weights and thresholds, the steepest gradient descent method in BP network can be used. The above part was originally my draft, because there is a little doubt about the RBF, and then read some

(Data_config[ ' Train_label ']) global_step=training_iters*model_config[ ' N_epoch '] decay_steps=training_iters*1 #global_step = tf. Variable (0, name = ' Global_step ', Trainable=false" Lr=tf.train.exponential_decay (Learning_rate=model_config[ Learning_rate '], Global_step=global_step, decay_steps=decay_steps, Decay_rate=0.1, Staircase=false, Name=none) optimizer= Tf.train.GradientDescentOptimizer (LR). Minimize (Cost,var_list=network.all_params) 1 2 3 4 5