搜索资源列表
Handwritten-Character
- 基于CNNs的手写字符识别系统，载入MNIST手写字符数据库，通过训练提取特征，达到99 的识别率-Based on CNNs handwritten character recognition system, load MNIST handwritten character database, extract features through training, up to 99 recognition rate
CNNS
- 这个程序旨在把卷积神经网络算法应用于手写字符识别。程序有几种结构的神经网络可以通过比较不同结构而得到对识别率的影响。-This program is designed to put the convolutional neural network algorithm is applied to the handwritten character recognition. The structure of the program there are several kinds of neural
cnn_toolkit_ver1.0
- 1D CNN和2D CNN网络的仿真实现，包含完整的子函数分解，采用matlab编写-demo_cnn_auto- demo for Autonomous CNNs with a symmetric A template solve_cnn2d- solve a 2D CNN system, Euler method solve_cnn2d_multi- solve a 2D multi-layered CNN system, Euler method Utility rout
DeepLearning-master
- 深度学习的概念源于人工神经网络的研究。含多隐层的多层感知器就是一种深度学习结构。深度学习通过组合低层特征形成更加抽象的高层表示属性类别或特征，以发现数据的分布式特征表示。[1] 深度学习的概念由Hinton等人于2006年提出。基于深信度网(DBN)提出非监督贪心逐层训练算法，为解决深层结构相关的优化难题带来希望，随后提出多层自动编码器深层结构。此外Lecun等人提出的卷积神经网络是第一个真正多层结构学习算法，它利用空间相对关系减少参数数目以提高训练性能。[1] 深度学习是机器
CNNs
- lr.py是用python实现了逻辑回归的源代码，并附带有注释。mlp.py是用python实现了多层感知机的源代码，并附带有注释。LeNetConvPoolLayer.py是用python实现了LeNet网络，并附带有注释。该文件需要引用mlp.py。-lr.py is python source code achieving a logistic regression , along with comments. mlp.py realized MLP by python, along wi
M-Tool-CNNs
- 深度学习之一，卷积神经网络例程，包括结构设置、系数更新、测试数据库等。-One deep learning, neural network convolution routines, including structural arrangement, the coefficient update, test s.
Convolutional-Neural-Networks-for-detecttion
- presented dictionary pair classifierdriven CNNs for object detection, where dictionary pair back propagation (DPBP) is proposed for the end-to-end learning of dictionary pair classifiers and CNN representation, and sample weighting is adopted
NiftyNet-dev
- 医疗3d处理，即基于卷积神经网络的医疗影像分析平台(NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis)
CNN
- 通过使用CNN来实现验证码识别的多任务学习(Using CAN to Achieve Multi-tasking Learning of Captcha Identification)
1D_CNNs
- 一维卷积神经网络在心电图数据训练中的应用 但是不包含标注数据(1d cnns for ECG data training)