simple neural network example

an F (a_k).for hidden layers , we use the chain rule:K represents the next layer of neurons in all J neurons. This formula means that the effect of j neurons on the target error function is achieved only by all. by (5.51) (5.48-49), you can getCalled the inverse conduction law. Here, you can probably see why it is called "reverse conduction", can be further understood from Figure 5.7: Error propagation is from the output layer by layer. The residuals are computed through the output layer and th

layer of neurons in all J neurons. This formula means that the effect of j neurons on the target error function is achieved only by all. by (5.51) (5.48-49), you can getCalled the inverse conduction law. Here, you can probably see why it is called "reverse conduction", can be further understood from Figure 5.7: Error propagation is from the output layer by layer. The residuals are computed through the output layer and the last layer of parameters is calculated and then propagated back.Finally,

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

distribution or probability model of the predicted results and samples of the degree of fit. The lower the confusion, the better the degree of fit. The calculation of the confusion histogram is shown in Flow 2:Figure 2 The construction process of the confusion histogram. (a) Sampled-area instances of the sensed region, (b) the excitation of the neurons in each area of the perceptual region, the color mapping of the excitation value, (c) the excitation of a series of neurons in the layer is tran

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

used in many natural language processing (Natural Language processing, NLP), so search RNN can search a lot of data, Therefore, this article only from the perspective of their own understanding of the principle of rnns and how to achieve, the latter will be specially sent a blog with the actual source code for analysis and learning: 1. The basic principle and derivation of RNN2. About RNN1. The basic principle and derivation of RNN(1) What is Rnns?? The purpose of Rnns is to use to process sequ

layouts into one part of the actual object. The subsequent layers will combine these parts to achieve the recognition of objects, which is often done through a fully connected layer. For deep learning, these features and hierarchies do not need to be artificially designed: they can all be obtained through a common learning process.2 the training process of neural networkAs shown in 1, the architecture of the deep learning model is generally stacked b

element I. The goal of the BP algorithm is to minimize . For example, , the mean square error is:. In the gradient drop, each iteration is updated according to the following formula:. the idea of the BP algorithm is as follows: Given a sample , all the activation values in the neural network are calculated first based on the forward conduction (forward propagati

layouts into one part of the actual object. The subsequent layers will combine these parts to achieve the recognition of objects, which is often done through a fully connected layer. For deep learning, these features and hierarchies do not need to be artificially designed: they can all be obtained through a common learning process.2 the training process of neural networkAs shown in 1, the architecture of the deep learning model is generally stacked b

). The neurons in the adjacent two layers are all connected, but there is no connection between them in the same layer. Now the parameters are described: x=[(x (1)) T, (x (2)) T,..., (x (m)) T]t x=\left[(x^{(1)}) ^t, (x^{(2)}) ^t,\ldots, (x^{(M)}) ^t\right]^t is the original input dataset. For a single input sample, x (i) =[x (i) 1,x (i) 2,..., x (i) n]t x^{(i)}=\left[x^{(i)}_1,x^{(i)}_2,\ldots,x^{(i)}_n\right]^t, i.e. each sample has n n features that correspond to the number of neurons in the

really simple, very mathematical beauty. Of course, as a popular science books, it will not tell you how harmful this method is.Implementation, you can use the following two algorithms:①KMP: Put $w_{i}$, $W _{i-1}$ two words together, run once the text string.②ac automaton: Same stitching, but pre-spell all the pattern string, input AC automaton, just run once text string.But if you are an ACM player, you should have a deep understanding of the AC au

whether it's good for example. A neural network is a combination of different neurons. The first layer is the input layer, the last layer is the output layer, and all the layers in the middle are hidden layers. Note: input unit x1, x2, X3, again, sometimes you can also draw an additional node x0. Meanwhile, there are 3 neurons here, I wrote A1 (2), A2 (2) and A

Overfitting and regularization (overfitting and normalization)Our network is no longer able to be extended to test data after the 280 iteration period. So this is not useful for learning. We say that the network is over-fitted (overfitting) or over-trained (overtraining) after the 280 iteration.Our network is actually learning the special case of the training dat

Part five The second model: convolutional neural NetworksDemonstrates the convolution operationLeNet-5-type convolutional neural network is the core of the great breakthrough in the field of computer vision recently. The convolution layer differs from the previous fully connected layer by using some techniques to avoid excessive number of parameters, but preserve

output.Displays the size of the resulting output image with a 3x3 grid on the 28x28 image using different step sizes and fill methods:The following is an understanding of the convolution process with two dynamic graphs:The first is a convolution process that is effectively populated with a 3x3 grid on a 5x5 image:The second is the convolution process with the same padding on the 5x5 image using a 3x3 grid, moving in the following way: http://cs231n.github.io/convolutional-networks/Reviewing the

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-l

Introduction to machine learning--talking about neural network This article transferred from: http://tieba.baidu.com/p/3013551686?pid=49703036815see_lz=1#Personal feel is very full, especially suitable for contact with neural network novice. Start with the question of regression (Regression). I have seen a lot of peopl

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 cla

') print "%s:loss after num_examples_seen=%d epoch=%d:%f"% (time, Num_examples_seen, epoch, Loss) # Adjust The learning rate if loss increases if (Len (losses) > 1 and losses[-1][ 1] > Losses[-2][1]): learning_rate = learning_rate * 0.5 print "Setting Learni NG rate to%f "% learning_rate Sys.stdout.flush () # added! Saving model Oarameters save_model_parameters_numpy ("./data/rnn-numpy-%d-%d-%s.npz"% (Self.hidden_dim, SE Lf.word_dim, time), self] # for each training

, height, and depth). Each layer of the convolution neural network changes the input data of the variable to the activation data of neuron 3D and outputs. In this example, the red input layer is the image, so its width and height is the width and height of the image, its depth is 3 (representing the red, green, blue 3 color Channels). Convolution