[机器学习] opencv学习

说明： opencv资料以及源码好东西 ，的很细致，很有用
<pan252932727> 上传 | 大小：20mb

[机器学习] NLP highlights of 2018

说明： It has been a big year for the field of natural language processing (NLP) and for machine learning as a whole. There have been many trends and breaking stories, with state-ofthe-art results and new interesting research directions emerging. We owe al
<blackkettle> 上传 | 大小：2mb

[机器学习] A Mathematical Introduction to Data Science

说明： 这本书是讲数据科学理论的，是北大教授姚远写的，难度较大。This monograph aims to provide graduate students or senior graduates in applied mathematics, computer science and statistics an introduction to data science from a mathematical perspective. It is focused around a central
<blackkettle> 上传 | 大小：3mb

[机器学习] RRT算法MATLAB实现

说明： 自己写的一段实现移动机器人路径规划的RRT的MATLAB代码
<zhenshui1234> 上传 | 大小：6kb

[机器学习] easy_install

说明： Python包管理工具，easy_install setuptools-40.6.3.zip
<caicai0001000> 上传 | 大小：820kb

[机器学习] An Introduction to Statistical Learning

说明： 统计学习经典书籍，适合从事数据分析的科研和运维人员。
<nigelchow> 上传 | 大小：9mb

[机器学习] BP网络实现有限个T/L分类

说明： 用BP算法实现有限个T/L识别，给出了输入层到隐含层、隐含层到输出层的权值。
<weixin_43343497> 上传 | 大小：4kb

[机器学习] 遗传算法的原理及其应用综合介绍

说明： 遗传算法的原理及其应用综合介绍，带你了解什么是遗传算法(Genetic Algorithm，GA)，GA可以用来做什么，当下GA的研究热点。
<qq_37053885> 上传 | 大小：1mb

[机器学习] 人脸识别 Demo

说明： 人脸识别Demo Python代代码 需要下载第三方模块使用程序
<hfutkk> 上传 | 大小：89mb

[机器学习] Dictionary Learning in Visual Computing

说明： e last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniq
<giscl> 上传 | 大小：5mb

[机器学习] Domain Adaptation for Visual Recognition

说明： Domain adaptation is an active, emerging research area that attempts to address the changes in data distribution across training and testing datasets. With the availability of a multitude of image acquisition sen- sors, variations due to illuminatio
<giscl> 上传 | 大小：2mb

[机器学习] Kernels for Vector-Valued Functions A Review

说明： Kernel methods are among the most popular techniques in machine learning. From a regularization perspec- tive they play a central role in regularization theory as they provide a natural choice for the hypotheses space and the regularization function
<giscl> 上传 | 大小：581kb