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Hoare Logic Notes

The Hoare assignment axiom \[\vdash \{P[E/V]\} V:=E \{P\} \]The Floyd assignment axiom \[\vdash \{P\} V:=E \{\exist v.\ (V=E[v/V]) \wedge P[v/V]\} \]Precondition strengthening \[\frac{\vdash P \Rightarrow P',\vdash\{P'\}C

dremio 22.1.1 发布

Python 数据可视化库 matplotlib 学习

库内模块 Module Class Notes

2022 NUS summer workshop visual computing phrase1 notes

一、需要准备的知识： 1.基本的编程能力，本课程需要使用的编程语言是python 2.微积分 3.线性代数 4.概率统计二、项目介绍 ⚠️：不会使用到深度学习，本课程介绍基础的visual computing，这些知识是在学习深度学习之前应该使用到的基础知识 project1:Traffic sign recognition 识别

7.5 $\text{Math Notes}$

$\large\text{Date: 7.5}$ $\text{OI Maths}$ $\text{I - CRT}$ 一句话： $\large Ans=\sum\limits_{i=1}^nr_iM_i\operatorname{inv}(M_i, m_i) (\mod M)$ （$\large M_i=\dfrac{M}{m_i},M=\prod m_i$） $\rm exgcd$ 求逆元： LL exgcd(LL a, LL

好看、好用、强大的手写笔记软件综合评测：Notability、GoodNotes、MarginNote、随手写、Notes Writers、CollaNote、CollaNote、Prodrafts、

与普通的笔记编辑器相比，手写笔记软件相对少一些。其中，比较出名的并不多。下面介绍一些比较主流、备受好评的，兼具有好看、好用、强大等特点的手写笔记软件。其中，首先介绍传统被忽略的两款笔记软件 OneNote 和苹果备忘录。随后测评了包括 Notability、GoodNotes、MarginNote、

6.28 $\text{String Notes II}$

$\large\text{6.28 Notes} $ $\text{String Notes II} $ $\text{Content: Suffix Array (Ex. Base Sort), Mismatch Tree}$ $\large\to\text{A Suffix Array Blog}\leftarrow$ inline void Monkey_Sort(int *a) { bool flag = true; while(flag) {

泛型容器类

泛型容器类 ArrayList<String> notes = new ArrayList<String>();泛型就是一种容器容器类有两个类型：容器的类型元素的类型其中 ArrayList就是容器的类型<>中的类型就是元素类型，如果元素类型为String那么存放数据就是String类型 private ArrayList<String> notes =

JAVA零基础泛型容器类

泛型容器类 ArrayList<String> notes = new ArrayList<String>();容器类有两个类型：　　容器的类型　　元素的类型其中ArrayList就是容器的类型 <>中的类型就是元素类型，如果元素类型为String那么存放数据就是String类型 private ArrayList<String> notes = new ArrayList<Strin

Andrew Ng Machine Learning Notes

Source: Coursera Machine Learning provided by Stanford University Andrew Ng - Machine Learning | Coursera Introduction definition: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if

Linux日志查看方法

1.Linux tail 命令 tail 命令可用于查看文件的内容，有一个常用的参数 -f 常用于查阅正在改变的日志文件。 tail -f filename 会把 filename 文件里的最尾部的内容显示在屏幕上，并且不断刷新，只要 filename 更新就可以看到最新的文件内容。命令格式： tail [参数] [文件] 常用参数

Deep Learning Week13 Notes

1. Attention for Memory and Sequence Translation Attention mechanisms aggregate features with an importance score that: depends on the feature themselves, not on their positions in the tensor relax locality constraints. $\Large\text{Note:}$ The a

Deep Learning Week12 Notes

1. Recurrent Neural Networks Temporal Convolutional Networks Such a model is a standard $1$d convolutional network, that processes an input of the maximum possible length. RNN and backprop through time The historical approach to processing sequences o

Deep Learning Week10 Notes

1. Auto-Regression Auto-regression methods model components of a signal serially, each one conditionally to the ones already modeled. They rely on the chain rule: \[\begin{align} P(X_1 = x_1,...,X_T= x_T) = P(X_1 = x_1)P(X_2=x_2|X_1=x_1)...P(X_T|X_{

Deep Learning Week9 Notes

1. Looking at parameters Hidden units of a perceptron one-hidden layer fully connected network $\mathbb{R}^2\rightarrow \mathbb{R}^2$ nb_hidden = 20 model = nn.Sequential( nn.Linear(2, nb_hidden), nn.ReLU(), nn.Linear(nb_h

Deep Learning Week8 Notes

1. Computer Vision Task Error rate: $P(f(X)\neq Y)$ Accuracy: $P(f(X)=Y)$ $\textbf{Balanced error rate (BER)}$: $\frac{1}{C}\sum_{y=1}^CP(f(X)\neq Y|Y=y)$ In two-class case, we can define $\textbf{True Positive (TP)}$ rate \(P(

CRISC Learning Notes 2 - Risk Governance

CRISC Learning Notes 2 - Risk Governance Four main objectives of risk governance: Establish and maintain a common risk view. Integrate risk management into the enterprise. Make risk-aware business decisions. Ensure that risk management controls are imple

Test

记录初学python的一系列小笔记，帮助自己学习，因为工作中可能需要python技术的补充。这些记录的小笔记只作为一些零散的前期准备。短期而言有学习两种语言的计划，一门前端语言，一门python，会对这两种新学语言进行自认为比较规整的整体性的技术学习记录。长期来看会一直更新技术类的文

Deep Learning Week6 Notes

1. Benefits of depth $\text{Consider ReLU MLPs with a single Input/Output, there exists a network }f$ \(\text{ with }D^* \text{ layers, and }2D^* \text{ internal units, such that, for any network }g\text{ with }D\text{ layers of sizes }\{W^{(1

Deep Learning Week3 Notes

1. Perceptron $\text{If }\sum_iw_ix_i+b\ge 0$ \[\begin{align} f(x)=1 \end{align} \]$\text{Otherwise, } f(x)=0$ $\large \textbf{Perceptron Algorithm:}$ $\text{Start with }w^0=0$ $\text{While }\exist n_k \text{ s.t. } y_{n

Deep Learning Week1 Notes

1. Tensors $\text{A tensor is a generalized matrix:}$ $\text{an element of }\mathbb{R^3} \text{ is a 3-dimension vector, but it's a 1-dimension tensor.}$ \(\large \text{The 'dimension' of a tensor is the number of indices.}\

notes

""" #Lambda表达式(lambda expression) 是一个匿名函数，Lambda表达式基于数学#λ演算得名，直接对应于其中的lambda抽象（lambda abstraction），是一个匿名函数，#即没有函数名的函数。Lambda表达式可以表示闭包。 #函数返回值表达式语句#利用Lambda函数 [然后这个是逆序]li=[{"age":20,"

Reading notes-8

Understanding and quantifying the global methane (CH4) buget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influ

Google Cloud资源层级, IAM Identity and Access Management, 控制台云交互

参考 https://dcclouds.qwiklabs.com/classrooms/1/notes/25216

【Notes】数据常用操作随笔

本人曾粗略学过numpy核心语法以及python的数据操作，但是奈何许久不写python了，并且np所学真到用时却不知很多功能如何实现。借此原因创建随笔，更新做Optimization过程中遇到的常用功能表达（py/py_np/RL/ML）。 *列出仅为常见用法，更多见Google 1/ np.argwhere(关于矩阵筛选条件) np