资源列表
symbol_resnet
- RACNN注意力机制，细腻度图片分类。 RA-CNN由上到下用了3个尺度并且越来越精细，尺度间构成循环，即上层的输出作为当层的输入。RA-CNN主要包含两部分：每一个尺度上的卷积网络和相邻尺度间的注意力提取网络(APN, Attention Proposal Network)。在每一个尺度中，使用了堆叠的卷积层等，最后接上全连接层于softmax层，输出每一个类别的概率；这个是很好理解的，代码采用的网络结构是VGG的网络结构。(RACNN attention mechanism)
tf_stock_1
- 利用lstm预测股票接下来100个时刻的股价，并作图(Using LSTM to Predict Stock Price)
CNN
- 该代码文件全称为卷积神经网络，这是深度学习神经网络里面处理图片比较好的网络。(The code file is called convolutional neural network, which is a better network for processing pictures in deep learning neural network.)
BP神经网络预测
- BP神经网络预测matlab的源代码，里面还附有公路运输的预测实例，只需修改数据即可。(BP neural network predicts the source code of matlab, which also includes a road transport prediction example, just need to modify the data.)
actor-critic
- 基于actor-critic的DDPG强化学习算法(DPG reinforcement learning algorithm based on actor-critic)
04.CNN处理CiFar
- 以python语言为基础，利用tensorflow机器学习架构，两层卷积神经网络实现，CiFar数据集图片分类功能。(Based on Python language, using tensorflow machine learning architecture, two-layer convolutional neural network, CiFar data set image classification function.)
AutoEncoder实战
- 深度学习中基于PyTorch架构的AutoEncoder 实例(Examples of AutoEncoder based on the PyTorch architecture in deep learning)
6_2_VGG
- 用thensorflow实现对VGGNET深度卷积神经网络的建立。(The establishment of the VGGNET convolutional neural network is implemented with the thenthraceflow.)
RBF自适应
- 基于梯度下降法RBF自适应神经网络控制(RBF adaptive neural network control based on gradient descent method)
SVM建模
- 介绍三种参数的SVM建模，分别预测股票价格(This paper introduces three parameters of SVM modeling, and predicts stock price separately.)
EKF
- 扩展卡尔曼滤波C语言代码，以及一些矩阵相关操作(Extended Kalman filter C code and some matrix related operations)
CNN_matlab
- 使用matlab2019a深度学习工具箱实现的CNN卷积神经网络分类例程，数据是随机生成的一维随机数(Using the CNN convolution neural network classification routine realized by MATLAB 2019a deep learning toolbox, the data is one-dimensional random number generated randomly)