tensorflow neural network

Source: Michael Nielsen's "Neural Network and Deep leraning"This section translator: Hit Scir master Xu Zixiang (Https://github.com/endyul)Disclaimer: We will not periodically serialize the Chinese translation of the book, if you need to reprint please contact [email protected], without authorization shall not be reproduced."This article is reproduced from" hit SCIR "public number, reprint has obtained cons

Neural Network Lecture VideoWhat are the neuronts?Storing numbers, returning function values for functionsHow are they connected?a1+ a2+ a3+ A4 +......+ An represents the activation value of the first levelΩ1ω2 ..... Ω7ω8 represents the weight valueCalculates the weighted sum, marks the positive weight value as green, the negative weight value is marked red, the darker the color, the closer the representati

Deep neural Network, the problem of pattern recognition, has achieved very good results. But it is a time-consuming process to design a well-performing neural network that requires repeated attempts. This work [1] implements a visual analysis system for deep neural

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

BP algorithm of neural network, gradient test, random initialization of Parameters neural Network (backpropagation algorithm,gradient checking,random initialization)one, cost functionfor a training set, the cost function is defined as:where the red box is circled by a regular term, K: the number of output units is the

potentials, are actually some faint currents. So if a neuron wants to deliver a message, it sends a faint current to other neurons through its axis bursts.2 , the yellow circle represents a neuron, X is the input vector, and θ represents the weight of the neuron (which is actually the model parameter we described earlier), and hθ (X) represents the excitation function (in neural network terminology, the ex

seen before, and if it has a similar word (similar in meaning) to the sentence we have seen, it will have a higher probability, so that it will gain generalization. It is challenging to train such a large model (with millions of parameters) within a reasonable time. The report that we use neural networks to compute probability functions shows that the method presented in two text corpora significantly improves the most advanced n-ary syntax model, an

Just entered the lab and was called to see CNN. Read some of the predecessors of the blog and paper, learned a lot of things, but I think some blog there are some errors, I try to correct here, but also added their own thinking and deduction. After all, the theory of CNN has been put forward, I just want to be able to objectively describe it. If you feel that there is something wrong with this article, be sure to tell me in the comments below.convolutional n