cnn current

Keras is a Theano and TensorFlow-compatible neural network Premium package that uses him to component a neural network more quickly, and several statements are done. and a wide range of compatibility allows Keras to run unhindered on Windows and MacOS or Linux.Today to compare learning to use Keras to build the following common neural network: Regression RNN regression Classification CNN Categories RNN classification Self-

The biggest problem with full-attached neural networks (Fully connected neural network) is that there are too many parameters for the full-connection layer. In addition to slowing down the calculation, it is easy to cause overfitting problems. Therefore, a more reasonable neural network structure is needed to effectively reduce the number of parameters in the neural network. convolutional Neural Networks (convolutional neural network,cnn) can do that.

The CNN Formula derivation 1 prefaceBefore looking at this blog, please make sure that you have read my top two blog "Deep learning note 1 (convolutional neural Network)" and "BP algorithm and Formula derivation". and has read the paper "Notes on convolutional neural Networks" in the literature [1]. Because this is the interpretation of the literature [1] The derivation process of the formula in the first part of the thesis 2

Transferred from: http://dataunion.org/11692.htmlZhang YushiSince July this year, has been in the laboratory responsible for convolutional neural networks (convolutional neural network,cnn), during the configuration and use of Theano and Cuda-convnet, Cuda-convnet2. In order to enhance the understanding and use of CNN, this blog post, in order to communicate with people, mutual gain. Before the text, say a

Introduction to convolutional Neural Networks Convolutional neural network is a multi-layer neural network that specializes in processing machine learning problems related to images, especially big images. The most typical convolutional network consists of a convolution layer, a pooling layer, and a full connection layer. The convolution layer works with the pooling layer to form multiple convolution groups, extract features layer by layer, and finally complete classification through several ful

AlexNet contribution : ILSVRC2012 champion, showing the depth of CNN in the image task of the astonishing performance, the upsurge of CNN research, is now deep learning and the rapid development of AI important reason. The Imagenet competition provides a platform for the Hinton that has been studying neural networks, Alexnet was published by Hinton and his two students, and deep learning has been sile

http://mp.weixin.qq.com/s?__biz=MjM5ODkzMzMwMQ==mid=2650408190idx=1sn= f22adfb13fb14f8a220222355659913f1. How to understand the status of NLP: see some tips for the latest doctoral dissertationIt may be a shortcut to look at the current status of an area and see the latest doctoral dissertation. For example, there are children's shoes asked how to understand the State-of-the-art of NLP, in fact, Stanford, Berkeley, CMU, JHU and other schools recently

Deploy a spark cluster with a Docker installation to train CNN (with Python instances) This blog is only for the author to record the use of notes, there are many details of the wrong place. Also hope that you crossing can forgive, welcome criticism correct. Blog Although the water, but also Bo master elbow grease also. If you want to reprint, please attach this article link , not very grateful!http://blog.csdn.net/cyh_24/article/

this is: in convolutional neural network CNN, the area of the input layer corresponding to an element in a certain layer of output is called the receptive field.???? Here is a partial link for reference:???? 1. The Field of sensation in deep neural networks (receptive fields)???? 2. What's a receptive field in a convolutional neural network????? 3. Painless understanding of the receptive field in CNN???? 4

How Dos attacks on CNN sites? Method 1. Direct access to this page http://www.chenmin.org/doscnn.html, Method 2. Save this page as an HTML file and open it in a browser. Enable scripting by prompting, and then automatically connect to the CNN site every 5 Seconds (note, it is automatic, as long as you do not close the browser window can be, and will not affect your other operations), If the whole world

Girshick, Ross. "Fast r-cnn." Proceedings of the IEEE International Conference on computer Vision. 2015.Following the 2014 RCNN, Ross Girshick introduced fast rcnn in 15, with a sophisticated and compact process that significantly increased the speed of target detection. The source code is available on GitHub.The reason why fast r-cnn is proposed is mainly because R-CNN