matlab neural network tutorial

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

Last but not least, the structure of convolution neural network is built on FPGA. The FPGA I use is Xilinx's xc6slx45, and the following is the final resource usage One of the most important design is to solve the problem of two-dimensional convolution, I used the shift RAM IP core But there's a problem with using it: you need to get rid of some invalid data. Specifically as follows:

traditional BP algorithm, so the parameters of the deep network will converge in a good position.RBM, through unsupervised training that iterates large amounts of data, can refine the more essential features of the training data, which is considered a good initial parameter.This example is written in Matlab, in order to use digital recognition to train a handwritten digit recognition of a deep

Code reference: Http://deeplearning.net/tutorial/lenet.html#lenetCode Learning: http://blog.csdn.net/u012162613/article/details/43225445Experiment code download for this section: Github2015/4/9Experiment 1: Using the tutorial recommended CNN structural Experimentlearning_rate=0.1n_cv= 20 # First-layer convolution core 20N_vc=50 #第二层卷积核50n_epochs=200batch_size=500n_hidden=500Experimental results:Experiment 2

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:

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