convolutional neural network explained

0-Background This paper introduces the deep convolution neural network based on residual network, residual Networks (resnets).Theoretically, the more neural network layers, the more complex model functions can be represented. CNN can extract the features of low/mid/high-lev

minimize the cost function to obtain parameters, in the neural network gradient descent algorithm has a special name called the inverse propagation algorithm. in the sample diagram of the neural network above, the input is directly connected to the hidden layer (hiddenlayer), and the output is called the output layer

, the main mechanism function of the neural network is set; 8 Set the ; 9 is used to select each network layer (it should be explained that the 1th layer refers to the hidden layer rather than the input layer), so that the number of neurons in the layer and the transfer function can be set at 10 and 11; 12 buttons can

Next: convolutional neural network for image classification-medium9 ReLU (rectified Linear Units) LayersAfter each convolutional layer, an excitation layer is immediately entered, and an excitation function is called to add the nonlinear factor, and the problem of linear irreducible is rejected. Here we choose the meth

convolutional neural Networks:step by step Welcome to Course 4 ' s-A-assignment! In this assignment, you'll implement Convolutional (CONV) and pooling (POOL) layers in NumPy, including both forward pro Pagation and (optionally) backward propagation. notation: We assume that you are already familiar with numpy and/or have completed the previous courses. Let ' s g

Deep Learning Neural Network pure C language basic Edition Today, Deep Learning has become a field of fire, and the performance of Deep Learning Neural Networks (DNN) in the field of computer vision is remarkable. Of course, convolutional neural networks are used in engineer

structure (1). Intuition of CNNIn deep learning book, author gives a very interesting insight. He consider convolution and pooling as a infinite strong prior distribution. The distribution indicates, all hidden units share the same weight, derived from certain amount of the input and has Parallel invariant feature.Under Bayesian statistics, prior distribuion is a subjective preference of the model based on experience. and the stronger the prior distribution is, the higher impact it'll has on th

implication of this is that the statistical characteristics of the part of the image are the same as the rest. This also means that the features we learn in this section can also be used in other parts, so we can use the same learning features for all the locations on this image. More intuitively, when a small piece is randomly selected from a large image, such as 8x8 as a sample, and some features are learned from this small sample, we can apply the feature learned from this 8x8 sample as a de

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

Introduction to neural network programming (2): What are we writing during socket writing? Http://www.52im.net/thread-1732-1-1.html 1. IntroductionThis article is followed by the first article titled Neural Network Programming (I): Follow the animation to learn TCP three-way handshakes and four waves, and cont

steps in front of it (explained later). The classic Rnns is shown below: Recursive neural network and its forward calculation diagram (Source: Nature) in time step The above diagram expands the Rnns into a full network. By unfolding, we get a full sequence of networks. For example, if we study a sentence sequence of 5

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

second layer of neurons. Each neuron has input and output. The input and output at the input layer are the property values of the training samples. For the input of the hidden layer and the output layer, the connection from unit I on the previous layer to Unit J is the output of unit I on the previous layer, but the threshold value of Unit J. The output of neurons in the neural network is calculated throug

be explained in the algorithm:1, about, is the error of the neuron.For output-layer neurons, the actual output of cell J is the true output of J based on the known class designator for a given training sample.For the hidden layer neurons, it is the connection right of unit K to Unit J in the next higher layer, but the error of unit K.The increment of weight is the increment of the threshold, which is the learning rate.For the derivation, a gradient d

" because of "mountain climbing". The stochastic neural networks to be explained in this paper: Simulated annealing (simulated annealing) and Boltzmann machines (Boltzmann machine) are capable of "mountain climbing" by certain probability to ensure that the search falls into local optimum. The comparison of this image can be see:There are two main differences between random

the input layer node, once forward pass, the output value is our forecast value. Easy Wrong PointSince this is so easy to understand, why is there an error in the implementation? Here are a few of the errors encountered: the input node, which is a node of the feature dimension of each sample. or one node per sample. It is wrong to think that each sample corresponds to an output node. The answer is an input node for each feature; bias is essential . Bias is a numerical offset that is not affecte

the second layer of neurons. Each neuron has input and output. The input and output at the input layer are the property values of the training samples. For the input of the hidden layer and the output layer, the link from unit I on the previous layer to Unit J is the output of unit I on the previous layer, but the threshold value of Unit J. The output of neurons in the neural network is calculated through

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

, 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:

)}} {\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