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

Back to Basics: The Abstract Machine Bob Steagall CppCon 2020 K E W B C O M P U T I N GCopyright © 2020 Bob Steagall K E W B C O M P U T I N G Overview/Goals • Describe abstract machines in general general • Describe the C++ abstract machine specifically • Language goals that drive its design • Role in program development and execution • Important definitions and characteristics • Important components components of the abstract machine, and their relationships • Provide a useful overview of the C++ abstract machine CppCon 2020 - The Abstract Machine 2Copyright © 2020 Bob Steagall K E W B C O M P U T I

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

Debugging the BPF Virtual Machine Lorenzo Fontana October 28, 2020 ● Debugging is useful to understand how things work ● Sometimes, eBPF programs can’t even load ● I couldn’t find good resources on this this, so, here I am ● I break lots of eBPF programs ● The BPF Virtual machine is not easy to understand Why ? The BPF subsystem lives in the kernel AND The kernel can be debugged using gdb The

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)

Machine Learning with ClickHouse Nikolai Kochetov, ClickHouse developer Experimental dataset NYC Taxi and Uber Trips › Where to download: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page

Machine Learning with ClickHouse Nikolai Kochetov, ClickHouse developer Experimental dataset NYC Taxi and Uber Trips › Where to download: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page

Solving Nim by the Use of Machine Learning Exploring How Well Nim Can be Played by a Computer Mikael Nielsen Røykenes Thesis submitted for the degree of Master in Informatics: Programming and Networks Solving Nim by the Use of Machine Learning Exploring How Well Nim Can be Played by a Computer Mikael Nielsen Røykenes c⃝ 2019 Mikael Nielsen Røykenes Solving Nim by the Use of Machine Learning http://www . . . . . . . . . 5 3.4 The Sprague-Grundy Theorem . . . . . . . . . . . . . . . . . . . 6 4 Machine Learning 6 4.1 Reinforcement learning . . . . . . . . . . . . . . . . . . . . . . . . 7 4.1.1 The

#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