搜索资源列表
neural-network
- 深度学习python实现，并附有MNIST上的测试程序，准确率98 以上-Deep learning learns low and high-level features large amounts of unlabeled data, improving classification on different, labeled, datasets. Deep learning can achieve an accuracy of 98 on the MNIST dataset.
NN
- 实现的一个用于手写数字识别的框架，可以设置神经网络结构，用的数据是mnist的(Implementation of a handwritten numeral recognition framework, you can set the neural network structure, the training data is MNIST)
tensorflow_cov_mnist
- 基于tensorflow的mnist数据集卷积神经网络简单代码实现。(MNIST dataset based on tensorflow convolutional neural network simple code implementation)
AlexNet
- 使用TensorFlow 实现 AlexNet ，并使用 Mnist 数据集进行训练并测试。(AlexNet is implemented using TensorFlow and trained and tested using the Mnist data set.)
Knn_train_mnist
- 利用Python实现Mnist数据集训练knn算法(Use Knn method to train mnist.)
LeNet5MNIST
- 使用TensorFlow处理MNIST数据，帮助更好的理解和使用TensorFlow(Using TensorFlow to process MNIST data to help better understand and use TensorFlow)
test1
- 神经网络，深度学习上非常经典的例子-RNN循环神经网络，使用mnist数据集，代码简单易懂，学习方便(Neural network, deep learning is a very classic example -RNN circular neural network, the use of mnist data sets, the code is easy to understand, easy to learn)
pytorch-vae-master
- 变分子编码 重构图像 Mnist 特征提取(vae reconstruction Mnist feature extracting)
dbn_tf-master
- 利用深度置信网络实现对mnist数据集的分类(sort out the set of mnist with DBN)
生成对抗网络
- 生成对抗网络针对mnist数据集，Python语言实现。(Generate confrontation network for MNIST data set, implemented in Python language.)