You are given two arrays A and B, each of size n. The error, E, between these two arrays is defined . You have to perform exactly k1 operations on array A and exactly k2 operations on array B. In one operation, you have to choose one element of the a…

终于打了一场CF,不知道为什么我会去打00:05的CF比赛…… 不管怎么样,这次打的很好!拿到了Div. 2选手中的第一名,成功上紫! 以后还要再接再厉! [A]Check the string 题意: 一个合法的字符串可以这样生成: 初始是一个空串,然后小 A 同学往这个串中加入若干(大于零)个字符\(\texttt{a}\),然后小 B 同学往这个串的末尾加入若干(大于零)个字符\(\texttt{b}\),最后小 C 同学往这个串的末尾加入一些字符\(\texttt{c}\),但要满足\(…

Error handling and Go go语言错误处理 12 July 2011 Introduction If you have written any Go code you have probably encountered the built-in error type. Go code uses error values to indicate an abnormal state. For example, the os.Openfunction returns a non-ni…

关于本课程的相关资料http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML17.html 错误来自哪里? error due to "bias" and error due to "variance" 当我们要求无穷多个数的平均值或者方差时,我们选取了N个样本出现计算.很显然我们得到的结果是存在一定误差的.当我们选区的样本的值越多的时候,我们得到的结果也就越准确.类比于训练模型,我们所选择的训练数据是有限的,很多时候我们希望他们…

李宏毅机器学习课程---3.Where does the error come from 一.总结 一句话总结:机器学习的模型中error的来源是什么 bias:比如打靶,你的瞄准点离准心的偏移 variance:比如打靶,你的实际打靶的位置 偏离你的瞄准点的距离:相当于方差 1.机器学习中为什么需要判断error的来源? 有的放矢,改进模型:因为你的模型出错,你肯定需要改进模型,知道错误来源后才方便改进模型 2.做多次实验,一次函数和多次函数的函数在图上如何分布? 多次函数在多次实验中分布的线…

转自:http://blog.evjang.com/2017/01/nips2016.html           Eric Jang Technology, A.I., Careers               Monday, January 2, 2017 Summary of NIPS 2016   The 30th annual Neural Information Processing Systems (NIPS) conference took place in Barcelona…

1. Introduction The Saga of Ryzom is a persistent massively-multiplayer online game (MMORPG) released in September 2004 throughout Europe and North America, localised in 3 languages so far. It has been developed by Nevrax since 2000, and was taken ov…

Deep Learning in a Nutshell: History and Training This series of blog posts aims to provide an intuitive and gentle introduction to deep learning that does not rely heavily on math or theoretical constructs. The first part in this series provided an…

The AlphaGo Replication Wiki 摘自:https://github.com/Rochester-NRT/RocAlphaGo/wiki/01.-Home Contents :  Home 01. Home 02. Code 03. Data 04. Neural Networks and Training 05. Supervised Policy Network (Phase I) 06. Reinforcement Policy Network (Phase II)…

Stephen Smith's Blog All things Sage 300… The Road to TensorFlow – Part 7: Finally Some Code leave a comment » Introduction Well after a long journey through Linux, Python, Python Libraries, the Stock Market, an Introduction to Neural Networks and tr…