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PredictionIO Alternative Setup: How to Build Custom ML Recommendation ...

Every recommendation engine needs these core elements: Data ingestion pipeline for user interactions and item metadata; Feature engineering to extract meaningful signals; Model training using collaborative or content-based approaches; Real-time inference for serving recommendations; Evaluation metrics to measure performance; Setting Up Your Development Environment

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The Ampere Porting Advisor Tutorial-It Industry-php.cn - php中文网

Running the Ampere Porting Advisor as a Python Script. Pr-requisites. Python 3.10 or above (with PIP3 and venv module installed). (Optionally) Open JDK 17 (or above) and Maven 3.5 (or above) if you want to scan JAR files for native methods. Unzip and jq is required to run test cases. Enable Python Environment. Linux/Mac:

https://toxigon.com/but-where-to-code-without-vba

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Excel Without VBA: Where to Code Instead? - Toxigon

discover the best alternatives to vba for excel automation. learn about python sheetflash power automate office scripts and google apps script. find out where to code without vba and boost your productivity

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(UE 5.5) Using python or a blueprint, is there a way to see if a joint ...

Given a skeleton, I have been trying to see if it has a “neck_01” joint for example. There doesn’t seem to a way as far as I can see. In python, I got so far as getting the bone tree nodes, but I don’t know how to progress into each BoneNode to get its name. This brings up another related question. Is there a way to know all of the options usable for get_editor_property()? I’ve run ...

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自动化部署k8s，自动化部署监控、告警、日志组件以及在k8s上用vLLM部署大模型详细图文教程-CSDN博客

文章浏览阅读202次，点赞4次，收藏3次。本文首先详细介绍了如何利用juju高度自动化部署高可用kubernetes集群，然后再利用juju部署COS（canonical开源的一套监控、告警以及日志套件），如何高效部署ceph集群。接着介绍了如何利用helm部署NVIDIA GPU operator最后详细说明了如何在kubernetes集群上运行vLLM，不仅 ...

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安培移植顾问教程 - php中文网

Originally, it was coded as a Python module that analyzed known incompatibilities for C and Fortran code. This tutorial walks you through building and using the tool and how to act on issues identified by the tool. The Ampere Porting Advisor is a command line tool that analyzes source code for known code patterns and dependency libraries.

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Dirichlet distribution - Wikipedia

In probability and statistics, the Dirichlet distribution (after Peter Gustav Lejeune Dirichlet), often denoted ⁡ (), is a family of continuous multivariate probability distributions parameterized by a vector α of positive reals.It is a multivariate generalization of the beta distribution, [1] hence its alternative name of multivariate beta distribution (MBD). [2]

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【Esp32】为 idf 定制本地 Arduino 组件 - CSDN博客

如果你还记得老周前文中说的内容，你会猜到，其实位于 main 目录下的 CMakeLists.txt 文件是描述 idf 组件的。没错，main 组件，默认会把项目目录下名为 main 的组件添加到构建列表中。所以： 1、位于项目目录下的 CMakeLists.txt 文件是配置整个项目的；

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Malayalam script - Wikipedia

Malayalam script (Malayāḷa lipi; ... The Vazhappally inscription issued by Rajashekhara Varman is the earliest example, dating from about 830 CE. [1] [2] ... Before the vertical bar virama used to cut through the main consonant and it led to the creation of the chillu letters.

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How Debian Notepad protects privacy-Golang-php.cn - php中文网

Debian Notepad itself does not have direct encryption, but you can protect your privacy and data security through the following methods: Encrypting strings with OpenSSL command line tool OpenSSL is a powerful encryption tool that can be used to encrypt and decrypt strings. For example, the command to encrypt a string using the AES-256-CBC algorithm is as follows: echo-n"YourStringToEncrypt ...

Dirichlet distribution

In probability and statistics, the Dirichlet distribution (after Peter Gustav Lejeune Dirichlet), often denoted ⁡ (), is a family of continuous multivariate probability distributions parameterized by a vector α of positive reals.It is a multivariate generalization of the beta distribution, hence its alternative name of multivariate beta distribution (MBD).

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Fact sheet

Beta

Dirichlet distribution

Parameters

K ≥ 2 {\displaystyle K\geq 2} number of categories (integer) α = (α 1, …, α K) {\displaystyle {\boldsymbol {\alpha }}=(\alpha _{1},\ldots,\alpha _{K})} concentration parameters, where α i > 0 {\displaystyle \alpha _{i}>0}

Support

x 1, …, x K {\displaystyle x_{1},\ldots,x_{K}} where x i ∈ {\displaystyle x_{i}\in } and ∑ i = 1 K x i = 1 {\displaystyle \sum _{i=1}^{K}x_{i}=1}

PDF

1 B (α) ∏ i = 1 K x i α i − 1 {\displaystyle {\frac {1}{\mathrm {B} ({\boldsymbol {\alpha }})}}\prod _{i=1}^{K}x_{i}^{\alpha _{i}-1}} where B (α) = ∏ i = 1 K Γ (α i) Γ (α 0) {\displaystyle \mathrm {B} ({\boldsymbol {\alpha }})={\frac {\prod _{i=1}^{K}\Gamma (\alpha _{i})}{\Gamma {\bigl (}\alpha _{0}{\bigr)}}}} where α 0 = ∑ i = 1 K α i {\displaystyle \alpha _{0}=\sum _{i=1}^{K}\alpha _{i}}

Mean

E = α i α 0 {\displaystyle \operatorname {E} ={\frac {\alpha _{i}}{\alpha _{0}}}} E = ψ (α i) − ψ (α 0) {\displaystyle \operatorname {E} =\psi (\alpha _{i})-\psi (\alpha _{0})} (where ψ {\displaystyle \psi } is the digamma function)

Mode

x i = α i − 1 α 0 − K, α i > 1. {\displaystyle x_{i}={\frac {\alpha _{i}-1}{\alpha _{0}-K}},\quad \alpha _{i}>1.}

Variance

Var = α ~ i (1 − α ~ i) α 0 + 1, {\displaystyle \operatorname {Var} ={\frac {{\tilde {\alpha }}_{i}(1-{\tilde {\alpha }}_{i})}{\alpha _{0}+1}},} Cov = δ i j α ~ i − α ~ i α ~ j α 0 + 1 {\displaystyle \operatorname {Cov} ={\frac {\delta _{ij}\,{\tilde {\alpha }}_{i}-{\tilde {\alpha }}_{i}{\tilde {\alpha }}_{j}}{\alpha _{0}+1}}} where α ~ i = α i α 0 {\displaystyle {\tilde {\alpha }}_{i}={\frac {\alpha _{i}}{\alpha _{0}}}}, and δ i j {\displaystyle \delta _{ij}} is the Kronecker delta

Entropy

H (X) = log B (α) {\displaystyle H(X)=\log \mathrm {B} ({\boldsymbol {\alpha }})} + (α 0 − K) ψ (α 0) − {\displaystyle +(\alpha _{0}-K)\psi (\alpha _{0})-} ∑ j = 1 K (α j − 1) ψ (α j) {\displaystyle \sum _{j=1}^{K}(\alpha _{j}-1)\psi (\alpha _{j})} with α 0 {\displaystyle \alpha _{0}} defined as for variance, above; and ψ {\displaystyle \psi } is the digamma function

Method of moments

α i = E (E (1 − E) V − 1) {\displaystyle \alpha _{i}=E\left({\frac {E(1-E)}{V}}-1\right)} where j {\displaystyle j} is any index, possibly i {\displaystyle i} itself

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