neural agility

Source: Michael Nielsen's "Neural Network and Deep leraning", click the end of "read the original" To view the original English.This section translator: Hit Scir master Xu Wei (https://github.com/memeda)Statement: We will be in every Monday, Thursday, Sunday regularly serialized 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" h

Summary: WithThe artificial neural network has been developed with the development of computational intelligence. The industry now considers that the classification of Neural Networks (NN) in artificial intelligence (AI) may not be appropriate, and that the classification of computational Intelligence (CI) is more descriptive of the problem. Some topics in evolutionary computing, artificial life and fuzzy l

I. Artificial neural element model1. Synaptic value (connection right)Each synapse is characterized by its weight, and the connection strength between each neuron is represented by the synaptic value. On synapses connected to neurons, the connected input signal enters the sum unit of the neuron by weighting the weights. 2. Summation UnitThe summation unit is used to calculate the synaptic weighting of each input signal and this operation forms a linea

Previous 4ArticleThis is a fuzzy system, which is different from the traditional value logic. The theoretical basis is fuzzy mathematics, so some friends are confused. If you are interested, please refer to relevant books, I recommend the "fuzzy mathematics tutorial", the National Defense Industry Press, which is very comprehensive and cheap (I bought 7 yuan ). Introduction to Artificial Neural Networks Artificial

This article first Huchi: HTTPS://JIZHI.IM/BLOG/POST/INTUITIVE_EXPLANATION_CNN What is convolutional neural network. And why it's important. convolutional Neural Networks (convolutional neural Networks, convnets or CNNs) are a neural network that has proved particularly effective in the field of image recognition an

this:According to our experience, if the alphabet can be moved to the center of the field of view, the difficulty of recognition will be reduced a lot, in favor of improving the recognition rate.In this case, if we can change the image to the standard size, we can increase the corresponding recognition rate.For objects of real knowledge, from different angles, there will be different manifestations, even for the letter recognition, the letter can appear rotating:If the image can be rotated, the

Deep learning "engine" contention: GPU acceleration or a proprietary neural network chip?Deep Learning (Deepin learning) has swept the world in the past two years, the driving role of big data and high-performance computing platform is very important, can be described as deep learning "fuel" and "engine", GPU is engine engine, basic all deep learning computing platform with GPU acceleration. At the same time, deep learning has become a new strategic d

lstm Neural network in simple and lucid Published in 2015-06-05 20:57| 10,188 Times Read | SOURCE http://blog.terminal.com| 2 Reviews | Author Zachary Chase Lipton lstm Recurrent neural network RNN long-term memory Summary:The LSTM network has proven to be more effective than traditional rnns, according to the introduction of the deep learning three Daniel. This paper is composed by UCSD, a doctoral stude

The introduction of convolution neural network Original address : http://blog.csdn.net/hjimce/article/details/47323463 Author : HJIMCE Convolution neural network algorithm is the algorithm of n years ago, in recent years, because the depth learning correlation algorithm for multi-layer network training provides a new method, and now the computer's computing capacity is not the same level of computing that y

Linear regression and logistic regression are sufficient to solve some simple classification problems, but in the face of more complex problems (such as identifying the type of car in the picture), using the previous linear model may not result in the desired results, and due to the larger data volume, the computational complexity of the previous method will become unusually large. So we need to learn a nonlinear system: neural networks.When I was stu

Neural network and deep learning the book has been read several times, but each time there will be a different harvest. DL field of paper, every day there will be a lot of new idea out, I think, in-depth reading classic books and paper, must be able to find Remian open problems, so there is a different perspective.Ps:blog is a summary of important contents in the main extract book.Summary section Neural