recurrent neural network tutorial

similar to the dimensionality reduction) method. Maximum pooling divides the input image into overlapping image matrix blocks, and each sub-region outputs its maximum value. The two reasons why the maximum pooling method is very effective in the visual processing problem are:(1) Reduce the computational complexity of the upper level by reducing the non-maximum value.(2) The result of pooling supports translation invariance. In the convolution layer, each pixel point has 8 orientations that can

ReproducedUser-awareLinks: https://www.zhihu.com/question/24827633/answer/91489990Source: KnowCopyright belongs to the author. Commercial reprint please contact the author for authorization, non-commercial reprint please specify the source.is usually explained by the chain rules .such as the following neural network Forward propagation For nodes, the net input is as follows:

This tutorial uses lasagne, a tool based on Theano to quickly build a neural network:1, the realization of several neural network construction2, Discussion data augmentation method3, discuss the importance of learning "potential"4, Pre-discussion training (pre-training)The a

http://cv-tricks.com/tensorflow-tutorial/save-restore-tensorflow-models-quick-complete-tutorial/What is a TF model: After training a neural network model, you will save the model for future use or deployment to the product. So, what is the TF model. The TF model basically contains