optimus-logger Config [Jenkins] search Jenkins ON-Prem optimus-logger configuration Back to Dashboard Project name optimus-logger Status Description Docker build optimum logger on git changes Branch Specifier (blank for 'any') */master Add Branch Delete Branch ## optimus-logger Config [Jenkins] Git executable Repository browser Default (Auto) ![Image](/uploads/documents/a/a/

# 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

### Apex.AI ## Practical memory pool based allocators for Modern C++ ## PREVIOUSLY ON cppcon • CppCon 2019: Safe Software for Autonomous Mobility With Modern C++ by Andreas Pasternak • Quote: “Memory then memory shall only be allocated and deallocated during non-realtime program phases ## Memory pool • Memory pools with fixed block sizes is a solution which addresses these issues: ○ It provides fragmentation with accurately-sized buckets It is easy to implement, understand and reason about ## Memory pool  ![Image](/uploa

21 4.1.5. 附加组件 Operator ..... 22 4.1.5.1. 关于 OpenShift Update 服务 ..... 22 4.1.5.2. 了解 Machine Config Operator ..... 23 第5章 了解 OPENSHIFT CONTAINER PLATFORM 开发 ..... 25 5.1. 关于容器化应用程序开发 ..... 可充当平台的基础，不再需要手动升级操作系统和 control plane 应用程序。OpenShift Container Platform Operator（如 Cluster Version Operator 和 Machine Config Operator）允许对这些关键组件进行简化的集群范围内管理。 Operator Lifecycle Manager (OLM) 和 OperatorHub 提供了相应的工具，可用于存储 RHCOS 用作所有集群机器的操作系统，则集群将管理其组件和机器的所有方面，包括操作系统在内。因此，只有安装程序和 Machine Config Operator 才能更改机器。安装程序使用 Ignition 配置文件设置每台机器的确切状态，安装后则由 Machine Config Operator 完成对机器的更多更改，例如应用新证书或密钥等。 ##### 3.1.1. OpenShift 集群支持的平台

## 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) synthesis, ...) ☐ Most implementations of recent deep learning papers ○ ... ## References • Machine Learning 2021 Spring Pytorch Tutorial • Official Pytorch Tutorials https://numpy.org/ Any questions

## 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

# 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

## Yandex ## Yandex # Machine Learning with ClickHouse ## Experimental dataset ## NYC Taxi and Uber Trips > Where to download: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page > with categorical features support out of the box for Python, R https://catboost.yandex Edit machine-learning decision-trees gradient-boosting gbm gödt python kaggle gpu-computing tutorial conf.d/models_config.xml /path/to/libcatboostmodel.so config>/path/to/models/*_model.xmlconfig> ## CatBoost