1.Visually Indicated Sounds 网址:http://vis.csail.mit.edu/ 通过视频预测敲打的声音 2.AI Porn Video Editor 代码网址:https://github.com/ryanjay0/miles-deep 色情视频分类器 3.A Neural Algorithm of Artistic Style 代码网址:https://github.com/fzliu/style-transfer 图片风格转换…

PyTorch 原文: https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html 参考文章: https://www.cnblogs.com/king-lps/p/8665344.html https://blog.csdn.net/shaopeng568/article/details/95205345 https://blog.csdn.net/yuyangyg/article/details/8001857…

https://www.quora.com/How-do-I-learn-machine-learning-1?redirected_qid=6578644   How Can I Learn X? Learning Machine Learning Learning About Computer Science Educational Resources Advice Artificial Intelligence How-to Question Learning New Things Lea…

##Advice for Applying Machine Learning Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the le…

https://emerj.com/ai-sector-overviews/machine-learning-in-finance/ Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chatbots, or search engines. Given the high volume, accurate histor…

昨天总结了深度学习的资料,今天把机器学习的资料也总结一下(友情提示:有些网站需要"科学上网"^_^) 推荐几本好书: 1.Pattern Recognition and Machine Learning (by Hastie, Tibshirani, and Friedman's ) 2.Elements of Statistical Learning(by Bishop's) 这两本是英文的,但是非常全,第一本需要有一定的数学基础,第可以先看第二本.如果看英文觉得吃力,推荐看一下下面…

转自:机器学习(Machine Learning)&深度学习(Deep Learning)资料 <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最…

转载:http://dataunion.org/8463.html?utm_source=tuicool&utm_medium=referral <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智…

转载:http://www.jianshu.com/p/b73b6953e849 该资源的github地址:Qix <Statistical foundations of machine learning> 介绍:<机器学习的统计基础>在线版,该手册希望在理论与实践之间找到平衡点,各主要内容都伴有实际例子及数据,书中的例子程序都是用R语言编写的. <A Deep Learning Tutorial: From Perceptrons to Deep Networks>…

Problems[show] Classification Clustering Regression Anomaly detection Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank…