当前位置：
首页 资源下载
搜索资源 - CUDA neural network
搜索资源列表
-
1下载：
CUDA应用：在CUDA上实现神经网络，识别手写数字,the implementation of a neural network with CUDA. Neural Network for Recognition of Handwritten Digits
-
-
0下载：
its a neural network mlp implementation in c++ cuda.
-
-
0下载：
文章介绍如何使用CUDA实现神经网络，并把他应用在GPU图像处理单元上。
-An Artificial Neural Network is an information processing method that was inspired by the way biological nervous systems function, such as the brain, to process information. It is composed of a large number of
-
-
0下载：
文章介绍如何使用CUDA实现神经网络，并把他应用在GPU图像处理单元上。- A Neural Network on GPU This article describes the implementation of a neural network with CUDA.
library I developed- mainly using the MS.NET
-
-
0下载：
改代码是基于CUDA GPU的神经网络的实施。-This article describes the implementation of a neural network with CUDA..
-
-
0下载：
CUDA应用：在CUDA上实现神神经网络，识别手写数字
-CUDA applications: the CUDA God neural network to recognize handwritten digits
-
-
1下载：
一种基于nvidia CUDA架构的BP神经网络程序源码，在G80，G992 GPU上能完成BP神经网络训练。速度较双核CPU提高十倍 可直接使用。
-Based on the completion of the BP neural network training on the G80, G992 GPU nvidia CUDA architecture BP neural network program source code. Faster than dual-core CPU to
-
-
0下载：
This project implement the convolution neural network on GPU. This reduce much time for training process. This project was written in C# and cuda
-
-
0下载：
这是利用神经网络来实现手写字符识别，起准确率已经达到99.26 ，可以继续调整参数达到更深层次的效果。需要自己搭建opencv环境。后期工作可以利用cuda对其更深层次的加速-This is achieved using a neural network handwritten character recognition, since the exact rate has reached 99.26 percent, can continue to adjust the parameters t
-
-
1下载：
这是在cuda平台上的一段模拟脑细胞活动的代码，使用了并行计算来实现神经网络。-This is the code section on cuda platform simulated brain cell activity, the use of parallel computing to realize the neural network.
-
-
1下载：
一个前馈反向传播人工神经网络的实现，并应用CUDA加速-Implementation of a feed-forward backpropagation artificial neural network using CUDA
-
-
0下载：
是一种神经网络框架，在其基础上可以实现物体检测，图像分类，模式识别功能。(Darknet is a neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. You can find the source on GitHub or you can read more about what Darknet can do righ
-
