recurrent neural network tutorial

What's RNN? The cyclic neural network, the recurrent neural network, is proposed mainly to deal with sequence data and what sequence data is. is the previous input and the back of the input is related, such as a word, before and after the words are related, "I am hungry, re

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

, we can directly use the full connection of the neural network, to carry out the follow-up of these 120 neurons, the following specific how to do, as long as the knowledge of multi-layer sensors understand, do not explain. The above structure, is only a reference, in the real use, each layer feature map needs how many, volume kernel size selection, as well as the pool when the sample rate to how much, and

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

single unit with a complex memory unit .??TensorFlow examples of LSTMHttps://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/tutorials/recurrent/index.mdhttp://colah.github.io/posts/2015-08-Understanding-LSTMs/It is mentioned herethat RNN can learn historical information when the distance is short, but RNN is powerless when the distance is longer . example of a short distance, predicting skylong-distance examples, predictions French??the

) # 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

growth are structured data 8. Question EighthAnswer: AC. This question examines our understanding of RNN (recurrent neural networks). RNN has achieved some success in speech recognition, language modeling, translation, picture description and other issues. It is a supervised learning, such as input data in English, labeled French. RNN can be seen as multiple assignments of the same

Objective From the understanding of convolution nerves to the realization of it, before and after spent one months, and now there are still some places do not understand thoroughly, CNN still has a certain difficulty, not to see which blog and one or two papers on the understanding, mainly by themselves to study, read the recommended list at the end of the reference. The current implementation of the CNN in the Minit data set effect is good, but there are some bugs, because the recent busy, the

It is important to understand how the chat robot (chatbots) works. A basic mechanism of chat bots is to use text classifiers for intent recognition. Let's look at how the Artificial neural network (ANN) works internally. In this tutorial, we will use the 2-layer neuron (a hidden layer) and the word bag (bag of words) method to organize our training data. There ar

Sequence to Sequence learning with NN"Sequence-to-sequence learning based on neural networks" was downloaded from the original Google Scholar.@author: Ilya sutskever (Google) and so onfirst, the total Overview Dnns has made remarkable achievements in dealing with many difficult problems. This paper mentions the problem of using a 2-layer hidden layer neural network