svm_numberByHand

deep learning一般是学习的层次结构，它也是有一定理论依据的，即模拟人脑的大脑皮层工作，因为大脑皮层的视觉区域也是分层次工作的，越底层的视觉皮层对那些底层特征就越敏感。综上所述，feature learning有这么多应用需求的驱动和生物神经理论上的支持，注定它能够在AI领域中发挥一定的作用。一些实验表明，有些feature learning学习到的特征几乎比所有其它的特征效果要好，比如本文中的ISA模型就是其中一个。(deep learning hierarchy of learning, it is also a certain theoretical basis that simulate the human brain' s cerebral cortex, visual areas of the cerebral cortex is hierarchical work, the more the bottom of the visual cortex is more sensitive to those underlying characteristics . In summary, feature learning so many application needs the support of the drive and biological neural theoretically doomed it to be able to play a certain role in the field of AI. Some experiments show that some feature learning learning to feature almost better than all the other features of the effect of the ISA model such as this is one.)

说明：  Another matlab file for transporter seller progrramming, as before

图像复原中的two-step iterative shinkage方法(two-step iterative shinkage)

我的这个源码可以直接绘制一个正圆。方便又实惠哦(mine is good,it can print a circle)

播放音乐，利用matlab来播放音乐，很好的一个代码(music play)

图像的二值化,不使用Matlab自带的语句处理，使用的是自己的编程语句。(Binary image, do not use Matlab s own statement processing, using their own programming statements.)

这个算法是信源数估计中经典的AIC算法，我已验证过。(This algorithm is the source of the AIC estimates classical algorithm, I have verified.)

在线盲去卷积，含有文献（Online Blind Image Deconvolution for Astronomy（2009））及MATLAB代码，希望对学习图像复原的朋友有所帮助(Online blind deconvolution, with the literature (Online Blind Image Deconvolution for Astronomy (2009)) and the MATLAB code, I hope to learn image restoration friends help)

matlab的图论工具箱，基本够用了，大家试一试。(matlab toolbox graph theory, basic enough, please try.)

用以计算迭代动力系统的Lyapunov指数.当此指数小于0时,系统是稳定的.(Iterative dynamic system for the purpose of calculating the Lyapunov index. When the index is less than 0:00, the system is stable.)