搜索资源列表
siliconpredictNARXwith-pca-VS-kpca
- 用PCA加动态神经网络（DNN）与KPCA加动态神经网络（DNN）做铁水含硅量预测，适用于多种时间序列预测。-the prediction of silicon content with NARX model based on PCA and DNN Vs the prediction of silicon content with NARX model based on KPCA and DNN .This method also can use to the other time serie
sourceCode
- 用python 语言结合DNN简单实现语音增强(DNN speech enhancement)
dnn
- 用TensorFlow搭建神经网络，识别手写数字(building the neural network by using TensorFlow to identify mnist dataset)
DNN
- 使用神经网络进行语音增强，包括语音特征提取，DNN，语音重构(Speech enhancement using neural networks)
DeepLearnToolbox-master
- DNN工具包，可以利用MATLAB实现深度神经网络。达到预测的目的(DNN toolkit can implement deep neural network using MATLAB. Achieve the purpose of prediction)
归档
- 神经网络拟合函数，DNN神经网络，TensorFlow(DNN function, using Tensorflow to conduct it)
deep-learning-opencv
- opencv3.3以上的版本调用caffe模型提前训练好的模型进行图片识别。(The above version of opencv3.3 calls the Caffe model in advance training model for image recognition.)
mnist98
- 改进的dnn，准确率达到了百分之98，有注释(The accuracy of the improved DNN is ninety-eight percent, with annotations)
DNN
- 利用python3完整实现DNN，包括前向传播和反向传播。实现一个2次函数的拟合。(Complete implementation of DNN using python3, including forward propagation and reverse propagation. Implement a quadratic function fitting.)
DNN
- 深度神经网络搭建，搭建一个深层的神经网络用于训练。(A deep neural network is built to build a deep neural network for training.)
MATLAB-DNN-master
- dnn的训练及搭建源代码，非常实用，对初学者是一个不错的起点，特别是深度学习方面的(Training and building source code of DNN)
DNN实现手写数字识别
- DNN实现手写数字的识别，准确率80以上，可以自行改变学习率等，希望能帮助到大家。