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These two days in the study of artificial neural networks, using the traditional neural network structure made a small project to identify handwritten numbers as practiced hand. A bit of harvest and thinking, want to share with you, welcome advice, common progress.The usual BP neural
al (Eds), Advances in Neural information processing Systems (NIPS 2006), MIT Press, 2007The following main principles are found in these three papers:Unsupervised learning expressed is used for (pre) training each layer;A level of unsupervised training at a time, followed by the level of the previous training. The expression learned at each level as input to the next layer;Use unsupervised training to adjust all layers (plus one or more additional la
handwritten fonts. Detailed code Download: http://www.demodashi.com/demo/13010.html Introduction of basic knowledgeNeural network basic knowledge of the introduction part contains a lot of formulas and graphs, using the Web site of the online editor, implementation is inadequate. I wrote a 13-page Word document, put in the understanding of the pressure pack, everyone download to see, I recorded a video, we can roughly browse a bit.Two, Python code im
Neural networks have many advantages over the traditional methods of classification tasks. Application: A series of WORKS2 managed to obtain improved syntactic parsing results by simply replacing the linear model of a parse R with a fully connected Feed-forward network. Straight-forward applications of a Feed-forward network as a classifier replacement (usually
isThe output at t time is not only dependent on the memory of the past, but also on what will happen later.
Deep (bidirectional) Recurrent Neural Network
Deep recurrent neural networks are similar to bidirectional recurrent neural networks,There are multiple layers in each duration.
Deep cyclic
LSTM (long-short term Memory, LSTM) is a time recurrent neural network that was first published in 1997. Due to its unique design structure, LSTM is suitable for handling and predicting important events with very long intervals and delays in time series. Based on the introduction of deep learning three Daniel, Lstm network has been proved to be more effective tha
follows:Development historydnn-Definitions and conceptsIn convolutional neural networks, convolution operations and pooling operations are stacked organically together, forming the backbone of the CNN.It is also inspired by the multi-layered network between the macaque retina and the visual cortex, and the deep Neural
TravelseaLinks: https://zhuanlan.zhihu.com/p/22045213Source: KnowCopyright belongs to the author. Commercial reprint please contact the author for authorization, non-commercial reprint please specify the source.In recent years, the Deep convolutional Neural Network (DCNN) has been significantly improved in image classification and recognition. Looking back from 2
(EMNLP 2014), 1746–1751.[2] Kalchbrenner, N., Grefenstette, E., Blunsom, P. (2014). A convolutional Neural Network for modelling sentences. ACL, 655–665.[3] Santos, C. N. DOS, Gatti, M. (2014). Deep convolutional neural Networks for sentiment analysis of the short texts.
Based on the traditional polynomial regression, neural network is inspired by the "activation" phenomenon of the biological neural network, and the machine learning model is built up by the activation function.In the field of image processing, because of the large amount of data, the problem is that the number of
is unroll into a vector, then using the existing gradient descent algorithm in the library to find the optimal parameters, and finally reshape into a matrix form; The reason for this is that the parameters of the ready-made gradient descent algorithm, the Inittheta requirement, must be in the form of a vector.3,gradient CheckingThis is a mathematical method to seek partial derivative.It can be used to verify that the gradient descent algorithm is implemented correctly, when the data of the two
, such as the number of hidden nodes, whether the step is fixed, and not discussed here.Prospect:There have been more researches on neural networks, and many new extension algorithms have been produced, such as convolutional neural networks, deep neural networks, and impulsive neur
number of hidden layers, the construction method as described above, the training according to the actual situation of the selection of activation function, forward propagation to obtain cost function and then use the BP algorithm, reverse propagation, gradient decline to reduce the loss value.
Deep neural networks with multiple hidden layers are better able to solve some problems. For example, using a neural
Civilization number" and the Central State organ "youth civilization" title.Smart Apps
Intelligent processing is the core problem
20w Human brain Power consumption
Multilayer large-scale neural network ≈ convolutional Neural Network + LRM (different feature
kernel and step operation, There may be the wrong dimension (analogy 2x3 matrix can not be multiplied by the 2x4 matrix, you need to replace the 2x4 matrix into a 3x4 matrix, here is the matrix of the 2x4 to add a row of 0 elements, so that it becomes the matrix of 3x4), the default is 0, preferably set to (kW-1)/ 2, which is the width of the convolution core 1 and then divided by 2. The padh default is PADW, preferably set to (kH-1)/2, which is the high-1 convolution core and then divided by 2
NIPS 2016 article: Intel China Research Institute on Neural Network compression algorithm of the latest achievementsHttp://www.leiphone.com/news/201609/OzDFhW8CX4YWt369.htmlIntel China Research Institute's latest achievement in the field of deep learning--"dynamic surgery" algorithm 2016-09-05 11:33 reproduced pink Bear 0 reviewsLei Feng Net press: This article is the latest research results of Intel China
BP (backward propogation) neural networkSimple to understand, neural network is a high-end fitting technology. There are a lot of tutorials, but in fact, I think it is enough to look at Stanford's relevant learning materials, and there are better translations at home: Introduction to Artificial neural
This paper is reproduced from http://blog.csdn.net/ironyoung/article/details/49455343
BP (backward propogation) neural networkSimple to understand, neural network is a high-end fitting technology. There are a lot of tutorials, but in fact, I think it is enough to look at Stanford's relevant learning materials, and there are better translations at home: Introdu
) # padding for I in range (self.size): Self.a[i] = Np.zeros (Self.n[i]) # full 0 Self.z[i] = Np.zeros (Self.n[i]) # full 0 Self.data_a[i] = Np.zeros (Self.n[i]) # Full 0 if I
The complete code below is what I have learned from the Stanford Machine Learning tutorial, completely self-tapping:
Import NumPy as NP "Reference: Http://ufldl.stanford.edu/wiki/index.php/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C" class Neuralnetworks (object): "" Def __init__ (s
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