卷积神经网络matlab代码 不需要配置，直接将工作目录设为这个压缩包的解压完的目录下就行，matlab直接运行

The first CNN appeared in the work of Fukushima in 1980 and was called Neocognitron. The basic architectural ideas behind the CNN (local receptive fields,shared weights, and spatial or temporal subsampling) allow such networks to achieve some degree of shift and deformation invariance and at the same time reduce the number of training parameters. Since 1989, Yann LeCun and co-workers have introduced a series of CNNs with the general name LeNet, which contrary to the Neocognitron use supervised training. In this case, the major advantage is that the whole network is optimized for the given task, making this approach useable for real-world applications. LeNet has been successfully applied to character recognition, generic object recognition, face detection and pose estimation, obstacle avoidance in an autonomous robot etc. myCNN class allows to create, train and test generic convolutional networks (e.g., LeNet) as well as more general networks with features: - any directed acyclic graph can be used for connecting the layers of the network; - the network can have any number of arbitrarily sized input and output layers; - the neuron’s receptive field (RF) can have an arbitrary stride (step of local RF tiling), which means that in the S-layer, RFs can overlap and in the C-layer the stride can differ from 1; - any layer or feature map of the network can be switched from trainable to nontrainable (and vice versa) mode even during the training; - a new layer type: softmax-like M-layer. The archive contains the myCNN class source (with comments) and a simple example of LeNet5 creation and training. All updates and new releases can be found here: http://sites.google.com/site/chumerin/projects/mycnn

详细解读了卷积神经网络是如何工作的，从CNN卷积层、激活层、池化层到全链接层，及多层CNN作用进行了通熟易懂的讲解

第一章回顾了理解卷积神经网络的动机；  第二章阐述了几种多层神经网络 ，并介绍当前计算机视觉领域应用中最成功的卷积结 构；  第三章具体介绍了标准卷积神经网络中的各构成组件 ，并从生物学和理论两个角度分 析不同组件的设计方案 ；  第四章讨论了当前卷积神经网络设计的趋势及可视化理解卷积神经网络的相关研究工 作 ，还重点阐述了当前结构仍存在的一些关键问题

详情参考https://github.com/LianHaiMiao/pytorch-lesson-zh/，这个老师讲的特别详细，或者参考https://blog.csdn.net/a1103688841/article/details/89222614

基于卷积神经网络的SAR图像目标识别研究

设计并编写程序来实现线性卷积运算，可以在重叠相加法和重叠保留法中任选一种方法实现 要求给出输入信号和输出信号的图形描述，以及简要的说明 给出计算中间过程的图形描述及简要说明

Matlab卷积和源代码 利用matlab编写卷积和

包含cifar10数据集，CNN卷积网络源码，OpenMV IDE2.2，PPT

fpga-ml-促进剂 该存储库托管用于卷积神经网络的基于FPGA的加速器的代码，有关整个设计和设计原理的非常详细的说明， 。 该存储库以前位于