## Amazon AI 构建于深度学习之上的智能服务 Amazon Rekognition Amazon Machine Learning Amazon Polly Amazon EMR Amazon Lex Spark & Spark ML AI Services AI Platforms Apache MXNet TensorFlow Caffe Torch ## 应用案例: Capital One “As a heavy user of AWS, Amazon Lex’s seamless integration with other AWS services like AWS Lambda and AWS DynamoDB is really appealing.” CapitalOne $ ^{®} $ “A highly scalable

# Machine Learning Lecture 10: Neural Networks and Deep Learning Feng Li fli@sdu.edu.cn https://funglee.github.io School of Computer Science and Technology Shandong University Fall 2018 ## Deep Feedforward $ is usually a highly non-linear function • Feedforward networks are of extreme importance to machine learning practitioners - The conventional neural networks (CNN) used for object recognition from

# Apache RocketMQ on Amazon Web Services 部署手册 顾明 版本：v1.0.0 最后更新时间: 2021 年 01 月 # 亚马逊云科技 Copyright (c) 2021 by Amazon.com, Inc. or its affiliates. ## 目录 背景介绍.....3 架构.....3 部署说明.....5 快速部署 。针对 AMAZON WEB SERVICES 客户需要在 AMAZON WEB SERVICES 上使用 RocketMQ 的需求，我们开发了一键部署的方案，帮助客户快速的在自己的账号部署一个基于 EC2 的高可用的 RocketMQ 集群。 ## 架构 AMAZON CloudFormation 提供了一种创建和管理相关 AMAZON WEB SERVICES 资源的简便方法，并通过有序且 案部署到已有 VPC 中，将跳过 (不创建) 带有星号 (*) 的组件，并提示 您目前现有的配置。 按照默认 RocketMQ 的部署参数部署完成后，该方案会在用户的 AMAZON WEB SERVICES account 下部署如下的一个架构，包含两个 Nameserver 互为备份，三个 Broker Instance 每个 Broker Instance 上面启动三个 Broker 实例，每个

• Managing translations • Reviewing source strings • Promoting the translation • Translation progress reporting • Administrators guide • Quick setup guide • Installation Access control • Translation projects • Language definitions • Continuous translation • Translation process • Checks and fixups • Machine translation • Addons • Component Lists • Optional Weblate modules • Translation workflows • Translation access • Translation states • Direct translation • Peer review • Dedicated reviewers • Enabling reviews

## Machine Learning Pytorch Tutorial TA：曾元(Yuan Tseng) 2022.02.18 ## Outline Background: Prerequisites & What is Pytorch? • Training & Testing Neural Networks in Pytorch • Dataset & Dataloader [Image](/uploads/documents/7/6/e/1/76e1a67e96719ae74c41b110fe07bfe6/p3_2.jpg) ## What is PyTorch? - An machine learning framework in Python. • Two main features: ○ N-dimensional Tensor computation (like NumPy) & speech) ESPnet (speech recognition, translation, synthesis, ...) ☐ Most implementations of recent deep learning papers ○ ... ## References • Machine Learning 2021 Spring Pytorch Tutorial • Official

## Debugging the BPF Virtual Machine eBPF Summit ## Why? ● Debugging is useful to understand how things work ● Sometimes, eBPF programs can’t even load - I couldn’t find good resources on this, so so, here I am ● I break lots of eBPF programs - The BPF Virtual machine is not easy to understand ## The approach ## The BPF subsystem lives in the kernel ![Image](/uploads/documents/7/1/6/1/7161fc3

Back to Basics: The Abstract Machine Bob Steagall CppCon 2020 ## KEWB COMPUTING ## Overview/Goals • Describe abstract machines in general • Describe the C++ abstract machine specifically • Language goals • Important components of the abstract machine, and their relationships • Provide a useful overview of the C++ abstract machine ## What is an Abstract Machine? “Abstract machines are machines because between the high level of a programming language and the low level of a real machine. The instructions of an abstract machine are tailored to the particular operations required to implement operations of

# 09.Services Web # 19 décembre 2023 # Développement web il3 Services web HE-Arc (DGR) 2022 ## Applications distribuées • Motivation : répartir l’exécution sur plusieurs machines – Principe : Les Les composants/services communiquent par le réseau - Problèmes : Hétérogénéité systèmes, langages, ... – Solution : Protocole générique, abstraction différences - Exemples : RPC, RMI (java), CORBA, DCOM pour une application aux services offerts aux humains • Service web = webapp pour une autre application : – Webapps : pour humains, via un navigateur (HTTP + HTML) – Services web : aux autres applications

# Lecture Notes on Support Vector Machine Feng Li fli@sdu.edu.cn Shandong University, China ## 1 Hyperplane and Margin In a n-dimensional space, a hyper plane is defined by $$ \omega^{T}x+b=0 $$ 7b9d33d07aa393893fb3862bcd67/p2_1.jpg) Figure 1: Margin and hyperplane. ## 2 Support Vector Machine ### 2.1 Formulation The hyperplane actually serves as a decision boundary to differentiating positive we can construct a infinite number of hyperplanes, but which one is the best? Supported Vector Machine (SVM) answers the above question by maximizing $ \gamma $ (see Eq. (6)) as follows $$ \begin

## Lecture 6: Support Vector Machine Feng Li Shandong University fli@sdu.edu.cn December 28, 2021 ## Outline  1 SVM: moving it parallelly along $ \omega $ (b < 0 means in opposite direction) ## Support Vector Machine • A hyperplane based linear classifier defined by $ \omega $ and b • Prediction rule: $ y = [Image](/uploads/documents/4/1/3/0/41305aec36322a1beae76d1d88486b9a/p14_2.jpg) ## Support Vector Machine (Primal Form) • Maximizing $ 1/\|\omega\| $ is equivalent to minimizing $ \|\omega\|^{2}=\omega^{T}\omega