1Brief History of Machine Learning
机器学习文献综述, 从感知机神经网络决策树 SVMAdaboost 到随机森林 Deep Learning.
2 Deep Learning in Neural Networks: An Overview
88 页的深度学习文献综述
3 A Gentle Introduction to Scikit-Learn: A Python Machine Learning Library
假设您是一位 python project 师并且想深入的学习机器学习, 那么这篇文章也许能够帮助到你
4 Choosing a Machine Learning Classifier
该怎样选择机器学习算法, Naive Bayes,Logistic Regression,SVM, 决策树等方法的优劣, 另外讨论了样本大小 Feature 与 Model 权衡等问题
5 An Introduction to Deep Learning: From Perceptrons to Deep Networks
深度学习概述: 从感知机到深度网络, 作者对于样例的选择理论的介绍都非常到位, 由浅入深, 翻译版本: http://www.cnblogs.com/xiaowanyer/p/3701944.html
来源: https://mp.weixin.qq.com/s?__biz=MzI3NTkyMjA4NA==&mid=2247485357&idx=2&sn=f368fd86dcb269707b8d1f6ac7ea6cee&chksm=eb7c2a66dc0ba3706f3d53d6ead66c5f4119b1b9453a943d48906d458bd243774757ab6890a8#rd