caffe neural network

catalog view Summary view Subscription [Top] "convolutional neural network-evolutionary history" from Lenet to AlexnetTags: CNN convolutional neural Network Deep learningMay 17, 2016 23:20:3046038 people read Comments (4) favorite reports Classification:"Machine Learning Deep Learning" (a)Copyright NO

It took a week to learn about neural networks after soy sauce in the Knowledge Engineering Center. The teacher arranged a question and asked me to try it. I did a little simple. I conducted several groups of tests and wrote a summary report. I posted it here. After more than a week of experimentation, I have a simple understanding of this issue. The following is my thoughts on this issue. In the last two days, I suddenly felt that the problem was much

BP (Back Propagation) network is a multi-layer feed-forward Network trained by the error inverse propagation algorithm, which was proposed by a team of scientists led by Rumelhart and mccelland in 1986, it is one of the most widely used neural networks. The BP network can learn and store a large number of input/output

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

BP (back propagation) network is the 1986 by the Rumelhart and McCelland, led by the team of scientists, is an error inverse propagation algorithm training Multilayer Feedforward Network, is currently the most widely used neural network model. BP network can learn and store

Dry Goods | The latest development of speech recognition framework--deep full sequence convolution neural network debut2016-08-05 17:03 reprinted Chenyangyingjie 1 reviewsIntroduction: At present the best speech recognition system uses two-way long-term memory network (LSTM,LONGSHORT), but the system has high training complexity, decoding Singo problems, especial

Generalized regression neural network GRNN (General Regression neural Network) Generalized regression Neural network is an improvement based on radial basis function neural

convolutional Neural Network (convolutional neural network,cnn), weighted sharing (weight sharing) network structure reduces the complexity of the model and reduces the number of weights, which is the hotspot of speech analysis and image recognition. No artificial feature ex

Series PrefaceReference documents: Rnnlm-recurrent Neural Network Language Modeling Toolkit (click here to read) Recurrent neural network based language model (click here to read) EXTENSIONS of recurrent neural NETWORK LAN

Recurrent neural network language modeling toolkit source code (8), recurrentneuralReferences: RNNLM-Recurrent Neural Network Language Modeling Toolkit (Click here to read) Recurrent neural network based language model (read he

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 2014 to 2016 of these two years more time, has

, database storage of things more, a lot of things are known to know do not know what. Second, the database index is fast and complete, according to a thing can quickly associate with the principle of its occurrence. Third, the sensory ability is strong, palpation all sharp. That's what makes Sherlock Holmes.Because I know so much, so when I see a paper that blends decision-making forests with convolutional neural networks, I feelsomething is more clo

Deep Learning Neural Network pure C language basic Edition Today, Deep Learning has become a field of fire, and the performance of Deep Learning Neural Networks (DNN) in the field of computer vision is remarkable. Of course, convolutional neural networks are used in engineering to reduce computational workload rather t

features, for each feature has 255 values;For such an image, if the use of two characteristics, there are about 3 million features, if it is also a logical return, the calculation of the cost is quite largeThis time we need to use the neural network.2. Neural network Model Representation 1The basic structure of the

The principle of RBF neural networks has been introduced in my blog, "RBF Neural Network for machine learning", which is not repeated here. Today is to introduce the common RBF neural Network learning Algorithm and RBF neural

BP neural network The concept of BP neural network is a multilayer feedforward neural network, its main characteristic is: the signal is forward propagation, and the error is the reverse propagation. Specifically, for the followin

The basic overview of neural networks and neural network models are not carefully introduced here. A detailed introduction to the introduction of the neural network and its model is presented in the details of Daniel Ng, Stanford University. This paper mainly introduces the

Tutorial Content:"MATLAB Neural network principles and examples of fine solutions" accompanying the book with the source program. RAR9. Random Neural Networks-rar8. Feedback Neural Networks-rar7. Self-organizing competitive neural networks. RAR6. Radial basis function