Tic-Tac-Toe Online Server Base on the Tic-Tac-Toe Game of CS188, Berkeley, I develop an online version of Tic-Tac-Toe. Now your agent can play with my agent online! I think it is a good way to check whether our agents are optimal
The Question Fish eating fruit on jisuanke Given an undirected acyclic graph G, all possible path P in the graph, calculate: The first taste In the contest, a handsome foriegn teammate conviced me that this problem can be solve using LCA. I tried. And it
Overview Regions with CNN features: Efficient Graph Based Image Segmentation use disjoint set to speed up merge operation Selective Search HOG (Histogram of Oriented Gradient) Multiple criterions (color, texture, size, shape) to merge regions AlexNet/VGG16 R-CNN Notice that many
Useful Materials Distinctive Image Features from Scale-Invariant Keypoints[1] by David G. Lowe. SIFT(Scale-Invariant Feature Transform)[2] on Towards Data Science. The SIFT (Scale Invariant Feature Transform) Detector and Descriptor[3]. Notes Uses DoG (Difference
Types of Noise Additive noise Additive noise is independent from image signal. The image g with nosie can be considered as the sum of ideal image f and noise n.[1] Multiplicative noise Multifplicative noise is often dependent on image signal. The relation of image and

Greek Alphabet

Greek Alphabet Letter (Capital Case) Letter (Lower Case) Name English Equivalent alpha a beta b gamma g delta d epsilon e zeta z eta h theta th
Information Content where, I : the information content of X. An X with greater I value contains more information. P : the probability mass function. : b is the base of the logarithm used. Common values of b are 2 (bits),
AutoTag AutoTag is a program that generate tags for documents automatically. The main process includes: Participle (N-gram + lookup in dictionary) Generate bag-of-words for each document. Calculate term frequency and inverse document frequency. Pick top x words with greater


w3m: WWW wo Miru (c) Copyright Akinori ITO w3m is a pager with WWW capability. It IS a pager, but it can be used as a text-mode WWW browser. Keyboard Shortcuts Shortcut Action Level H Help Brower q Quit w3m

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Permission Control for NTFS We often encounter the problem that to mount NTFS under Linux means no permission control. But that is not true. According to JanC's Answer on AskUbuntu: Contrary to what most people believe, NTFS is a POSIX-compatible filesystem, and it is



List # list filter table sudo iptables -L # list nat table sudo iptables -L -t nat Redirect # Redirect locally sudo iptables -A OUTPUT -t nat -p tcp --src --dport 80 -j REDIRECT --to-port 8080
Major and Minor Step The step between each note below is a half: C C#(Db) D D#(Eb) E F F#(Gb) G G#(Ab) A A#(Bb) B C Steps in Major and Minor S: a half step T: a
Substitution, dirname, basename and suffix Substitution can be used to get path and short filename. filename=a/b/c/name.file echo ${filename#*/} # b/c/name.file echo ${filename##*/} # name.file echo ${filename%/*
Input and Output $ ffmpeg -i input.mp4 output.mp4 Cutting Video or Audio Begin and length: $ ffmpeg -i input.mp4 -ss 00:00:10 -t 01:00:10 output.mp4 Beginning time and ending time: $ ffmpeg -i input.mp4 -ss 01
0. install GPU drivers sudo add-apt-repository ppa:graphics-drivers/ppa sudo apt update sudo apt install nvidia-390 1. install cuda tookit and cudnn SDK (and CUPTI) Following the instructions at Installing Tensorflow on Ubuntu. # Adds NVIDIA package repository. sudo apt-key
This article is my reflection on my previous work FaceLock, a project to recognize user's face and lock the computer if the user doesn't present in a certain time. CNN is used to recognize different faces. I watch the Coursera course Convolutional Neural Networks by
This article is about some squashing functions of deep learning, including Softmax Function, Sigmoid Function, and Hyperbolic Functions. All of these three functions are used to squash value to a certain range. Softmax Function Softmax Function: A generalization of the logistic function that "squashes" a
This article is my learning note of the coursera course Sequence Models by Andrew Yan-Tak Ng. There are two typical RNN units of the hidden layers of the RNN according to Andrew Ng. One is GRN (Gated Recurrent Unit), the other is LSTM (Long Short
Transformation Defines a struct ObjectProperty as follow: property type comment rotation vec3 The object's rotation in its object center. scale vec4 / vec3 The object's scale level in its object center. translation vec3 The object's relative position to the


Time Complexity With partition, the elements compared each time are as follow: n, n/2, n/2, n/4, n/4, n/4, n/4, ..., 1, 1, ..., 1 (n ones) And their sum is: n, n,
以下皆为在开发我的 C++ 库 donnylib 的时候遇到的问题。 (一天遇到这么多 weird problems 也是很走运了) donnylib : https://github.com/Donny-Hikari/donnylib Weird Template Subclass 如下的程序会导致无法自动推断模板类型。(编译环境
Python 实现: AdaBoost - Donny-Hikari - Github Introduction AdaBoost 是 Adaptive Boosting 的简称。 Boosting 是一种 Ensemble Learning 方法。 其他的 Ensemble Learning 方法还有 Bagging, Stacking 等。 Bagging, Boosting, Stacking 的区别如下: Bagging: Equal weight voting. Trains
Android Library To build a android library project, simply change the following line in build.gradle (Module): apply plugin: 'com.android.application' to: apply plugin: 'com.android.library' To pack the library into a jar file, add these lines to build.gradle



This is a learning note of Logistic Regression of Machine Learning by Andrew Ng on Coursera. Hypothesis Representation Uses the "Sigmoid Function," also called the "Logistic Function": Which turn linear regression into classification. Sigmoid function looks like this: give us the probability that
Concept 上下界网络流 顾名思义,比普通的网络流多出了下界。 考虑将下界的流量限制进行转化。将原来的边(e, v1->v2)拆边,拆出一条上界为 max-min 的边(e1),和一条上界为 min 的边(e2, 虚拟存在
Problem 合怪升级,每只怪有他的等级和攻击力,以及是否为 tuner monster. 合怪必须一只 tuner monster 与一只 non-tuner monster 合,并且他们的等级之和必须等于合成的怪。只有特定等级和攻击力的怪可以被合成。某些合成怪必须
Introduction 最精彩的证明是自动证明。今天在知乎上看到的一位大佬对莫比乌斯反演的证明让我体会到了数论的神奇。坚定了我投靠数学的决心。 Mobius Inversion Formula 若存在 则有 反之亦然。 Basic Concept 1. Convolution /
Problem 给定数字串 S,求如下表达式的值: Solution 用前缀和转化该式。令 , 则 . 原式转化为: 二项展开,得: 将内外求和对调,使得可以预处理前缀和的和的p次方。式子化为: