© 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 张孝峰 AWS解决方案架构师 March 17, 2017 Amazon 的人工智能&深度学习 围绕数据的“飞轮” 机器学习 深度学习 人工智能 更多的用户 更好的产品 更多的数据 更好的分析 对象存储 数据库 数据仓库 数据流分析 商业智能 One skill for Alexa.” “As a heavy user of AWS, Amazon Lex’s seamless integration with other AWS services like AWS Lambda and AWS DynamoDB is really appealing.” Amazon Rekognition 基于深度学习的图像识别服务 目标和场景检测

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 f(x) is usually a highly non-linear function • Feedforward networks are of extreme importance to machine learning practioners • The conventional neural networks (CNN) used for object recognition from photos

Page 1 of 18 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 Copyright (c) 2021 by Amazon.com, Inc. or its affiliates. Page 外的互联网公司。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 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

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 ● Tensors year ■ ref: link1, link2 Some knowledge of NumPy will also be useful! What is PyTorch? ● An machine learning framework in Python. ● Two main features: ○ N-dimensional Tensor computation (like NumPy) NLP & speech) ○ ESPnet (speech recognition, translation, synthesis, ...) ○ Most implementations of recent deep learning papers ○ ... References ● Machine Learning 2021 Spring Pytorch Tutorial ● Official

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 ωT x + b = 0 (1) where ω ∈ Rn defined as γ = min i γ(i) (6) 1 ? ? ! ? ! 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 γ (see Eq. (6)) as follows max γ,ω,b γ s.t. y(i)(ωT

Lecture 6: Support Vector Machine Feng Li Shandong University fli@sdu.edu.cn December 28, 2021 Feng Li (SDU) SVM December 28, 2021 1 / 82 Outline 1 SVM: A Primal Form 2 Convex Optimization Review (b < 0 means in opposite direction) Feng Li (SDU) SVM December 28, 2021 3 / 82 Support Vector Machine A hyperplane based linear classifier defined by ω and b Prediction rule: y = sign(ωTx + b) Given: " such that min& ' & !() & + " = 1 Feng Li (SDU) SVM December 28, 2021 14 / 82 Support Vector Machine (Primal Form) Maximizing 1/∥ω∥ is equivalent to minimizing ∥ω∥2 = ωTω min ω,b ωTω s.t. y(i)(ωTx(i)

#IstioCon Is Your Virtual Machine Really Ready-to-go with Istio? Kailun Qin, Intel Haoyuan Ge #IstioCon Quick Summary (from Google Cloud Next ’19 [1]) VM works on Istio! [1] Istio Service Mesh Jianfei Hu, Google Cloud Next ‘19 #IstioCon Why Add VMs to the Mesh? ● = Why Service Mesh? ○ More services = more complexity ○ Need consistent policy enforcement ○ Need consistent metrics aggregation ● Traffic management ○ Load balancing for VMs, failover, A/B testing, modern rollouts for VM services ● Security ○ Enforce the same policies in the same way, across compute environments ● Observability

A Day in the Life of a Data Scientist Conquer Machine Learning Lifecycle on Kubernetes Brian Redmond • Cloud Architect @ Microsoft (18 years) • Azure Global Black Belt Team • Live in Pittsburgh, PA idle • Parallel training instead of sequential: huge time saver for large trainings Kubeflow • Machine Learning Toolkit for Kubernetes • To make ML workflows on Kubernetes simple, portable, and scalable

. . . . . . . . . 7 1.7 Creating Your First Virtual Machine . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.8 Running Your Virtual Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.8.5 Resizing the Machine’s Window . . . . . . . . . . . . . . . . . . . . . . 14 1.8.6 Saving the State of the Machine . . . . . . . . . . . . . . . . . . . . . . 14 1 10.2 Snapshot Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.11 Virtual Machine Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.12 Removing and Moving

. . . . . . . . . 7 1.7 Creating Your First Virtual Machine . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.8 Running Your Virtual Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.8.5 Resizing the Machine’s Window . . . . . . . . . . . . . . . . . . . . . . 14 1.8.6 Saving the State of the Machine . . . . . . . . . . . . . . . . . . . . . . 14 1 10.2 Snapshot Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.11 Virtual Machine Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.12 Removing and Moving