搜索资源列表
network_learn
- 简单地实现全神经网络，适合深度学习的基础入门。(Simple implementation of the whole neural network, suitable for in-depth study of basic entry.)
2、KNN学习
- KNN算法MATLAB仿真，KNN算法是经典的分类算法，是机器学习的基础算法(KNN algorithm, MATLAB simulation)
ml-startup-1
- 线型模型的学习资料 python源码，依赖sk-lean库实现(Linear model of learning materials Python source code, relying on sk-lean library implementation)
svm
- matlab实现的支持向量机，简易的实现代码学习分享(Matlab support vector machines, simple to achieve code learning to share)
javacv代码大全
- 本大全涵盖了所有开发javacv的ＡＰＩ代码，帮助ｊａｖａ程序员尽快掌握机器视觉。初级和高级人员都可以用的上的好文档。(This book covers all the development of javacv API code, to help Java programmers to grasp the machine vision as soon as possible. Good documentation available for both junior and senior per
p271-das.pdf
- 谷歌推荐算法论文，根据点击量进行推荐，十分经典，值得学习。(Google News Personalization: Scalable Online Collaborative Filtering)
《机器学习实战》源代码
- python人工智能机器人学习源代码，配套书籍为python机器人学习(Python artificial intelligence robot learning source code, supporting books for Python Robot Learning)
TENSORFLOW_MACHINE_LEARNING_COOKBOOK
- tensorflow与机器学习、深度学习的cookbook(tensorflow cookbook with machine learning, deep learning)
bp-分类器
- 这是bp神经网络的M文件，包括BP网络的第一阶段学习期（训练加权系数wki，wij)，BP网络的第二阶段工作期（根据局训练好的wki，wij和给定的输入计算输出），程序里有详细注释。该程序被用来作为分类器使用。(This is the BP neural network M files, including the first phase of BP network learning period (training weighting coefficient wki, wij), the se
65940018
- 具有机器学习的许多算法,对学习机器学习有很大帮助,希望在此能帮助到大家,()
libsvm.net-master
- SVR学习资源，用以建立C#引用的SVR(SVR learning resources to build C# referenced SVR)
fygal
- 一个用于支持向量机以及机器学习的软件机器源代码()
machine-learning-ex7
- 吴恩达机器学习课程，第八周作业，详解如何实现PCA以及kmeans(Andrew NG machine learning course, eighth weeks of assignment, detailed how to implement PCA and kmeans)
autocode
- 在高斯噪声下，使用深度学习进行符号调制，画出星座图(Under Gauss's noise, we use deep learning to make symbol modulation and draw a constellation)
House Price Regression
- 经典的房价预测问题，用jupyter notebook编写的简单实现。使用XGBoost模块进行学习预测。(The classic price prediction problem, a simple implementation written in jupyter notebook. The XGBoost module is used for learning prediction.)
ELM分类器
- ELM是基于深度学习的分类器，运算速度快。 在B_data.m里导入待分类矩阵B.mat（1-n列为特征值，n列为标签）；运行B_data.m；再打开fuzzyEn_main.m并运行即可。(ELM is based on depth learning classifier, computing speed. In B_data.m imported matrix to be classified B.mat (1-n as eigenvalues, n as a label); Run B
RBM学习算法
- 该文件主要包含了Matlab语言下开发的RBM机器视觉学习算法。(This document mainly includes the RBM machine vision learning algorithm developed under the Matlab language.)
CNN学习算法
- 该文件主要包含了CNN卷及神经网络Matlab算法，该算法主要用于完成机器视觉学习(The paper mainly includes CNN volume and neural network Matlab algorithm, which is used to complete machine vision learning.)
EM学习算法
- 该文件主要包含了Matlab条件下开发的EM算法，主要用于机器视觉学习。(This document mainly includes the EM algorithm developed under the condition of Matlab, which is mainly used for machine vision learning.)
RBF学习算法
- 该文件主要是在Matlab条件下开发的RBF算法，主要用于机器视觉学习分类(This file is mainly a RBF algorithm developed under the condition of Matlab, which is mainly used for machine vision learning classification.)