英雄预测分析——数据英雄预测分析——数据英雄预测分析——数据

机器学习决策树与分类方法课程报告，内部包含代码、算法思想、算法原理、算法分析、课程总结。机器学习决策树与分类方法课程报告，内部包含代码、算法思想、算法原理、算法分析、课程总结。机器学习决策树与分类方法课程报告，内部包含代码、算法思想、算法原理、算法分析、课程总结。机器学习决策树与分类方法课程报告，内部包含代码、算法思想、算法原理、算法分析、课程总结。机器学习决策树与分类方法课程报告，内部包含代码、算法思想、算法原理、算法分析、课程总结。机器学习决策树与分类方法课程报告，内部包含代码、算法思想、算法原理、算法分析、课程总结。机器学习决策树与分类方法课程报告，内部包含代码、算法思想、算法原理、算法分析、课程总结。机器学习决策树与分类方法课程报告，内部包含代码、算法思想、算法原理、算法分析、课程总结。机器学习决策树与分类方法课程报告，内部包含代码、算法思想、算法原理、算法分析、课程总结。机器学习决策树与分类方法课程报告，内部包含代码、算法思想、算法原理、算法分析、课程总结。机器学习决策树与分类方法课程报告，内部包含代码、算法思想、算法原理、算法分析、课程总结。机器学习决策树与分类方法课程报告

使用随机森林、SVM、线性回归等常用机器学习模型预测肺癌患病数据集的存活时长。 随机森林是一个包含多个决策树的分类器， 并且其输出的类别是由个别树输出的类别的众数而定。 支持向量机（Support Vector Machine, SVM）是一类按监督学习（supervised learning）方式对数据进行二元分类的广义线性分类器（generalized linear classifier），其决策边界是对学习样本求解的最大边距超平面。

Abstract The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent vectors to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably detect if an image is generated by a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model rede- fines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.

yolov3.weights是训练好的权重文件；yolov3.cfg是神经网络的结构文件；coco.names是算法的类别文件

向日葵图片资源（数据集）

模式识别与机器学习PRML 习题答案

使用BP神经网络、RBF神经网络以及PSO优化的RBF神经网络进行数据的预测

TensorFlow实战教程，图像识别应用示例教程，该课程特邀请AI100 阿里2017 云栖大会 API Solution大赛一等奖团队的联合创始人智亮先生，他将给大家介绍当前图像识别领域中先进的深度学习模型，并从源码级给大家讲解TensorFlow实现工业级图像识别应用的详细过程。具体内容包括：TensorFlow基础和模型选择，TensorFlow训练和验证模型，调参、模型上线和部署。

NASA关于涡扇发动机的仿真数据集，以文本形式打包，包含readme文件。可用于人工智能、机器学习等领域。 Engine degradation simulation was carried out using C-MAPSS. Four different were sets simulated under different combinations of operational conditions and fault modes. Records several sensor channels to characterize fault evolution. The data set was provided by the Prognostics CoE at NASA Ames.