- 2005-01-19_wntfsys nt file system 过滤的源代码
- celllbp 基于的LBP算法的细胞分类算法
- VFC32 V/F转换芯片驱动源代码
- Matlabgatoolbox 遗传算法工具箱还有实力以及源码 遗传算法工具箱还有实力以及源码
- boxgame VC++开发的一个界面较美观的推箱子游戏！程序较完美
- Employiee-Entery-Syatem Employiee Entery System This system develop for employee managemeou.In this system you can store all. information about Employee this is jointed in its company.This project developed for Rajratana Matel Indestry Employee management. In this system store all information about all of employee and its post and salary. In this system only admin show all data and information.
资源列表
贝叶斯分类算法
- 5个描述属性，一个分类属性，通过贝叶斯算法实现分类(5 descr iptive attributes, one categorical attribute, is implemented by Bayes algorithm.)
Deep Learning with Python(Fran?ois Chollet)
- 深度学习入门教程；使用python一步步教你走向大神之路(use python language to learn deeplearning)
New folder
- 采用粒子群算法对滑模控制器参数进行整定，对象为倒立摆(The particle swarm optimization algorithm is used to adjust the parameters of the sliding mode controller. The object is inverted pendulum.)
mtncarMatlab
- 强化学习qlearning编写，回归算法规划轨迹(Reinforcement learning qlearning, return algorithm to plan trajectory)
matlab Q学习仿真
- Qdemo演示程序 Qlearning Q学习主程序 调用 drnd（随机变量生成函数） 任务改变时，要设当改变execut子函数和一些脚标变换函数。 用于打印状态的statements也要改一下。(Qdemo demo The main program Qlearning Q learning Call DRND generating function (random variables))
Q_learning
- 强化学习代码，求解贝尔曼方程，用qlearning求解(Reinforcement learning code, behrman equation, using qlearning solution)
1天搞懂深度学习
- 深度学习很好的教程，适合研究深度学习的人。(Deep learning is a good tutorial for studying deep learners.)
实值编码遗传算法源程序
- 这是一个基于实值编码的加速遗传算法的MATLAB程序(This is a MATLAB program based on real coded genetic algorithm.)
MAgent-master
- 多智能体的一段代码，有关强化学习，机器学习，很实用的一段代码！(A code of multi-agent, about reinforcement learning, machine learning, a very practical piece of code!)
CNN-SVM
- 运用卷积神经网络来提取图片的特征值并用SVM做出分类(using CNN And SVM to train my pictures.)
LSTM
- LSTM对价格的预测 利用最新的神经网络 希望大家能够喜欢(29/5000 The LSTM's prediction of prices USES the latest neural networks to please everyone)
astar
- A*算法实现路径规划，返回的是离散的路径点，为启发式算法(Path planning by A* algorithm)