hinton neural networks

convolutional neural Network (CNN) is the foundation of deep learning. The traditional fully-connected neural network (fully connected networks) takes numerical values as input.If you want to work with image-related information, you should also extract the features from the image and sample them. CNN combines features, down-sampling and traditional

Transfer from http://blog.csdn.net/xingzhedai/article/details/53144126More information: http://blog.csdn.net/mafeiyu80/article/details/51446558http://blog.csdn.net/caimouse/article/details/70225998http://kubicode.me/2017/05/15/Deep%20Learning/Understanding-about-RNN/RNN (recurrent Neuron) is a neural network for modeling sequence data. Following the bengio of the probabilistic language model based on neural

parameter random initialization is introduced, we can combine my previous a neural network to get started knowledge http://blog.csdn.net/u012328159/article/details/ 51143536 See, believe can have a basic understanding of neural network. Note: Provide some reference material to everyone, can better help you understand the neural network better. Talk abo

relationship between word, extracted a lot of features. Which extracts the sentence features used by CNN. Convolution, Pooling,softmax, just a few processes. Le, Quoc v., and Tomas Mikolov."distributed representations of sentences and Documents." ICML (2014). Extension of the Word2vec model.In fact, we all feel that the deep model is able to extract images and other signals of the latent variables, then it should be very natural to extract the text topic out, LDA and so on is noth

+ 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

1 Figure Neural Network (original version)Figure Neural Network now the power and the use of the more slowly I have seen from the most original and now slowly the latest paper constantly write my views and insights I was born in mathematics, so I prefer the mathematical deduction of the first article on the introduction of the idea of neural Network Diagram

The use of neural networks to achieve autonomous driving, which means that the car through learning to drive themselves.It is a legend explaining how to realize automatic driving through neural network learning:The lower left corner is an image of the road ahead that the car sees. Left, you can see a horizontal menu bar (the direction indicated by the number 4),

called the output layer.　　　　For example, a superscript (2) Subscript 1 represents the first excitation of the 2nd layer, that is, the first excitation of the hidden layer. The so-called excitation (activation) refers to a specific neuron after reading the information, need to use the parameter matrix, after a series of calculations and then pass the value to the next layer, wherein the calculation process is S-excitation function or called the logical excitation function.Forward propagation for

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

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 class. The SMO network is a unsupervised learning algorithm similar to the previous Kmeans al

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 neural network with an

ExplainThis allows us to learn to predict a person ' s identity using a Softmax output unit, where the number of classes equals the Number of persons in the database plus 1 (for the final "not in Database" Class).Reasons for the above options error:1, plus 1 explanation error:Put someone's photo into the convolutional neural network, use the Softmax unit to output the kind, or label, to correspond to these different people, or not any of them, so in S

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 instability, which causes the learning of the front or back layer to stop.Vanishing gradient p

Why use convolution? In traditional neural networks, such as Multilayer perceptron (MLP), whose input is usually a feature vector, requires manual design features, and then the values of these features to form a feature vector, in the past decades of experience, the characteristics of artificial found is not how to use, sometimes more, sometimes less, Sometimes the selected features do not work at all (the

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 , you need to use more complex

, which is more robust to the change of image in space. DropoutFinally, a little mention of dropout, this is Hinton in improving neural networks by preventing co-adaptation of feature detectors[9] in the article. The method is that at the time of training, the node output of a layer of hidden layer output node is randomly selected p (such as 0.5), and the we

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

SOFTMAX layer. In this paper, the whole connection layer is used as the representation of the image. At the fully connected layer, the output of the fourth and third layers of max-pooling is used as the input of the fully connected layer, so that the local and global characteristics can be learned.5 Reference Resources [1] Http://deeplearning.stanford.edu/wiki/index.php/UFLDL%E6%95%99%E7%A8%8B Gardenia to Stanford Deep Learning research team's translation of deep learning tutorials

, then the SOFTMAX layer. In this paper, the whole connection layer is used as the representation of the image. At the fully connected layer, the output of the fourth and third layers of max-pooling is used as the input of the fully connected layer, so that the local and global characteristics can be learned.5 Reference Resources [1] Http://deeplearning.stanford.edu/wiki/index.php/UFLDL%E6%95%99%E7%A8%8B Gardenia to Stanford Deep Learning research team's translation of deep learning tut

Blog has migrated to Marcovaldo's blog (http://marcovaldong.github.io/) The tenth lecture of Professor Geoffery Hinton, neuron Networks for machine learning, describes how to combine the model and further introduces the complete Bayesian approach from a practical point of view. Why it helps to combine models In this section, we discuss why you should combine many models when making predictions. Using multip