资源列表
2
- 基于SVM算法和纹理特征提取的遥感图像分类(based on the SVM algorithm and texture feature extraction of remote sensing image classification)
SS-US-ELM
- Classification, Regression
Social-Profile-master冷启动
- 推荐Netflix，亚马逊系统Spotify，例如，可以推荐我们基于我们的历史的新东西，行为和我们玩什么内容。但是当一个新用户到达没有数据的系统时会发生什么呢？我们向他推荐什么？这是冷启动问题。(Recommending systems like Netflix, Spotify, amazon, for instance, can recommend us new stuff based on our history, behavior and what content we play. Bu
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.)
lssvm
- 最小二乘支持向量机回归，四个插入数据分别为训练输入、训练输出、测试输入、测试输出。工具包+程序(Least squares support vector regression (SVM), the four inserted data are training input, training output, test input and test output)
TensorFlow实战_黄文坚（完整）
- 教会你如何通过Python语言实现TensorFlow框架，作为一个深度学习的初学者，想要接触TensorFlow框架的初学者，是非常好的学习资料。(Teach you how to implement TensorFlow framework through the Python language, depth of learning as a beginner, want to contact TensorFlow framework of beginners, is a very good
RF_Reg_C
- 随机森林实现分类、预测的代码和一些相应的实例，啊啊啊啊啊(Random forest classification, prediction of the code and some corresponding examples, ah, ah, ah!)
大数据下的机器学习算法综述
- 研究大数据环境下的机器学习算法成为学术界和产业界共同关注的话题. 文中主要分析和总结当前用于处理大数据的机器学习算法的研究现状.(Developing machine learning algorithms for big data is a research focus. In this paper, the state of the art machine learning techniques for big data are introduced and analyzed.)
resnet-protofiles-master
- 卷积神经网络resnet18、resnet34、resnet50、resnet101、resnet152的配置文件（网络结构和解决方案文件）(Convolution neural network resnet18, resnet34, resnet50, resnet101, resnet152 configuration file (network structure and solution file))
DSI
- 一个很好用的DS证据理论工具箱，可以实现算法编程以及各种应用(A good toolbox of DS evidence theory)
rbf
- 自己编写RBF神经网络程序，RBF神经网络隐层采用标准Gaussian径向基函数，输出层采用线性激活函数，其中数据中心、扩展常数和输出权值均用梯度法求解，它们的学习率均为0.001。其中隐节点数选为10，初始输出权值取[-0.1，0.1]内的随机值，初始数据中心取[-1，1]内的随机值，初始扩展常数取[0.1，0.3]内的随机值，输入采用[0 1]的随机阶跃输入(Write your own RBF neural network, RBF neural network hidden layer
梯度下降
- 这是一篇精华文章，详细的说明了深度学习梯度下降算法的思想，并附有程序结果。(This is an essence of the article, detailed descr iption of the depth of learning, gradient descent algorithm ideas, along with program results.)