creating neural network

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 direc

http://colah.github.io/posts/2015-08-Understanding-LSTMs/ http://www.csdn.net/article/2015-11-25/2826323 Cyclic neural networks (recurrent neural networks,rnns) have been successful and widely used in many natural language processing (Natural Language processing, NLP). However, there are few learning materials related to Rnns online, so this series is to introduce the principle of rnns and how to achieve 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 cla

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

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

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 ,

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 m

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

I've been focusing on CNN implementations for a while, looking at Caffe's code and Convnet2 's code. At present, the content of the single-machine multi-card is more interested, so pay special attention to Convnet2 about MULTI-GPU support.where Cuda-convnet2 's project address is published in: Google Code:cuda-convnet2A more important paper on MULTI-GPU is: one weird trick for parallelizing convolutional neural NetworksThis article will also give an a

In the deep network, the learning speed of different layers varies greatly. For example: In the back layer of the network learning situation is very good, the front layer often in the training of the stagnation, basically do not study. In the opposite case, the front layer learns well and the back layer stops learning.This is because the gradient descent-based learning algorithm inherently has inherent inst

In the previous article "Artificial Neural Network (Artificial neural netwroks) Notes-Eliminate the sample order of the BP algorithm" to modify the weight of the method is called the "steepest descent method." Every time the weight of the changes are determined, the weight will be modified. Even to the simplest single layer perceptron. But we have a question, wh

UFLDL Learning notes and programming Jobs: multi-layer neural Network (Multilayer neural networks + recognition handwriting programming)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 l

1. Some basic symbols2.COST function================backpropagation algorithm=============1. To calculate something 2. Forward vector graph, but in order to calculate the bias, it is necessary to use the backward transfer algorithm 3. Backward transfer Algorithm 4. Small topic ======== ======backpropagation intuition==============1. Forward calculation is similar to backward calculation 2. Consider only one example, cost function simplification 3. Theta =======implementation Note:unrolling param

Http://blog.sina.com.cn/s/blog_98238f850102w7ik.htmlAll the current Ann neural network algorithm Daquan(2016-01-20 10:34:17)reproduced Tags: it Overview1 BP Neural network1.1 Main functions1.2 Advantages and Limitations2 RBF (radial basis function) neural network2.1 Main functions2.2

example, you is going to generate an image of the Louvre Museum in Paris (content image C), mixed with a painting By Claude Monet, a leader of the Impressionist movement (style image S). Let's see how you can do this. 2-transfer Learning Neural Style Transfer (NST) uses a previously trained convolutional network, and builds on top of. The idea of using a network

Single-layer perceptron does not solve the XOR problem Artificial Neural Networks (Artificial neural netwroks) have also fallen into low ebb due to this problem, but the multilayer Perceptron presented later has made the artificial neural network (Artificial neural netwroks

All the current Ann neural network algorithm DaquanOverview1 BP Neural network1.1 Main functions1.2 Advantages and Limitations2 RBF (radial basis function) neural network2.1 Main functions2.2 Advantages and Limitations3 Sensor Neural Network3.1 Main functions3.2 Advantages a

This is an extension of the discrete single output perceptron algorithm Related symbolic definitions refer to the artificial neural network (Artificial neural netwroks) Note-discrete single output perceptron algorithm Ok,start our Game 1. Initialization weight matrix W; 2. Repeat the following process until the training is complete: 2.1 For each sample (X,y)

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

+ 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