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

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 Multi Clouds #IstioCon Istio VM Integration is? A Tumultuous Odyssey… [1] Istio 1.8: A Virtual Machine Integration Odyssey, Jimmy Song #IstioCon V0.2 Mesh Expansion ● Prerequisites ○ IP connectivity workloads themselves #IstioCon V1.6-1.8 Better VM Workload Abstraction Item Kubernetes Virtual Machine Basic schedule unit Pod WorkloadEntry Component Deployment WorkloadGroup Service registry and

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

similar to the model that is discussed in the Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation paper. This model script is available on GitHub and NGC. similar to the model that is discussed in the Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation paper. PyTorch Release 23.06 PyTorch RN-08516-001_v23.07 similar to the model that is discussed in the Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation paper. This model script is available on GitHub and NGC.

. . . . . . . . . 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

. . . . . . . . . 7 1.8 Creating Your First Virtual Machine . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.9 Running Your Virtual Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.9.5 Resizing the Machine’s Window . . . . . . . . . . . . . . . . . . . . . . 14 1.9.6 Saving the State of the Machine . . . . . . . . . . . . . . . . . . . . . . 15 1 11.2 Snapshot Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.12 Virtual Machine Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.13 Removing and Moving