实用卷积神经网络:运用Python实现高级深度学习模型

卷积神经网络.pdf

卷积神经网络（高清版，详细介绍卷积神经网络原理，可编辑内容

卷积神经网络－高级篇.pdf

传统的目标检测任务主要通过人工提取特征模型建立，常用的特征包括：HOG、SIFT、Haar等，特征提取模型之后进行支持向量机或者Adaboost的分类任务，进而得到我们所关注的目标结果。由于特征模型的局限性，我们引入卷积特征，也就是经过卷积神经网络得到的特征信息，包括浅层信息和深层信息，浅层信息指的是：前级的卷积层得到的特征图，感受野更加关注的是图像细节纹理等特征。深层信息包括：后级的卷积层卷积得到的特征图信息，在语义语境方面更加抽象的高层信息。人工神经网络是根据大脑神经突触联接的结构进行信息处理的数学模型，视觉模拟系统通过稀疏编码的方式组合成为合理并且高效的图像处理系统。

图像理解中的卷积神经网络pdf

图像理解中的卷积神经网络pdf 图像理解中的卷积神经网络pdf

CNN 卷积神经网络

mdCNN is a Matlab framework for Convolutional Neural Network (CNN) supporting 1D, 2D and 3D kernels. Network is Multidimensional, kernels are in 3D and convolution is done in 3D. It is suitable for volumetric input such as CT / MRI / video sections. But can also process 1d/2d images. Framework supports all the major features such as dropout, padding, stride, max pooling, L2 regularization, momentum, cross entropy, MSE. The framework Its completely written in Matlab, No dependencies are needed. It is pretty optimized, when training or testing all of the CPU cores are participating using Matlab Built-in Multi-threading. There are several examples for training a network on MNIST, CIFAR10, 1D CNN, and MNIST3d - a special expansion of MNIST dataset to 3D volumes. MNIST Demo will download the dataset and start the training process. It will reach 99.2% in several minutes. CIFAR10 demo reaches about 80% but it takes longer to converge. For 3D volumes there is a demo file that will creates a 3d volume from each digit in MNIST dataset, then starts training on the 28x28x28 samples. It will reach similar accuracy as in the 2d demo This framework was used in a project classifying Vertebra in a 3D CT images. =~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~ To run MNIST demo: Go into the folder 'Demo/MNIST' , Run 'demoMnist.m' file. After 15 iterations it will open a GUI where you can test the network performance. In addition layer 1 filters will be shown. To run MNIST3D demo: Go into the folder 'Demo/MNIST3d' , and run 'demoMnist3D.m' file. =~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~ Check the 'mdCNN documentation.docx' file for more specification on how to configure a network For general questions regarding network design and training, please use this forum https://groups.google.com/forum/#!forum/mdcnn-multidimensional-cnn-library-in-matlab Any other issues you can contact me at hagaygarty@gmail.com Please use matlab 2014 and above

CNN 卷积神经网络

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

卷积神经网络（CNN）

很全面，很深刻的卷积神经网络（CNN）原理讲解。

vivado2019.2平台中通过verilog实现CNN卷积神经网络包括卷积层,最大化池化层以及ReLU激活层+操作视频

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cnn卷积神经网络PPT详解

cnn卷积神经网络

基于CNN卷积神经网络的目标分类训练和测试matlab仿真【含操作视频】

1.领域：matlab，CNN卷积神经网络的目标分类算法 2.内容：基于CNN卷积神经网络的目标分类训练和测试matlab仿真【含操作视频】 3.用处：用于CNN卷积神经网络的目标分类算法编程学习 4.指向人群：本硕博等教研学习...

MATLAB实现CNN卷积神经网络时间序列预测（完整源码和数据）

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MATLAB实现CNN卷积神经网络多输入回归预测（完整源码和数据）

MATLAB实现CNN卷积神经网络多输入回归预测（完整源码和数据） 数据为多输入回归数据，输入7个特征，输出1个变量。 运行环境MATLAB2018b及以上。

卷积神经网络CNN

讲解CNN基本结构，卷积运算和池化运算

CNN_卷积神经网络_

卷积神经网络编程简易实现，python语言编写

完整的CNN卷积神经网络

完整的CNN卷积神经网络，可用于图像等领域，有很好的指导。

标准CNN（卷积神经网络）

卷积神经网络CNN是深度学习的一个重要组成部分，由于其优异的学习性能（尤其是对图片的识别）。近年来研究异常火爆，出现了很多模型LeNet、Alex net、ZF net等等。由于大多高校在校生使用matlab比较多，而网上的教程代码基本都基于caffe框架或者python，对于新入门的同学来说甚是煎熬，所以本代码采用matlab结合MNIst手写数据库完成对手写数字的识别。本人水平有限，如有纰漏，还望各路大神，帮忙指正。

MATLAB实现CNN卷积神经网络多特征分类预测（完整源码和数据）

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CNN卷积神经网络程序

机器学习代码 CNN卷积神经网络 可直接运行

基于CNN卷积神经网络的煤矸石自动分选研究.pdf

基于CNN卷积神经网络的煤矸石自动分选研究.pdf

基于Transformer和CNN卷积神经网络的网络入侵检测python源码+数据集+详细注释.zip

基于Transformer和CNN卷积神经网络的网络入侵检测python源码+数据集+详细注释.zip基于Transformer和CNN卷积神经网络的网络入侵检测python源码+数据集+详细注释.zip基于Transformer和CNN卷积神经网络的网络入侵检测...

cnn卷积神经网络

利用卷积神经网络对mnist数据集进行分类，代码采用python进行编写，并有详细的注释，且文件自带mnist数据集。用户需要搭建好tensorflow环境配合python即可运行。

卷积神经网络CNN.ppt

卷积神经网络CNN.ppt

CNN.rar_CNN_CNN神经网络_卷积神经网络

整理的有关卷积神经网络的介绍，算法原理，和一些关键代码

CNN卷积神经网络PYTHON

CNN卷积神经网络，包含数据，代码有标注，可以用来参考学习

1. CNN卷积1

1.CNN的卷积核是单层的还是多层的 2.什么是卷积 3.什么是CNN的池化层 4.面试题 卷积的物理意义是什么 1.CNN的卷积核是单层的还是多层的 2.什么

【提供操作视频】基于多尺度CNN卷积神经网络的MRF图像分割算法matlab仿真

1.领域：FPGA，多尺度CNN卷积神经网络的MRF图像分割算法 2.内容：【提供操作视频】基于多尺度CNN卷积神经网络的MRF图像分割算法matlab仿真 3.用处：用于多尺度CNN卷积神经网络的MRF图像分割算法编程学习 4.指向...

基于CNN卷积神经网络模型的手写英文字母识别项目源码.zip

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