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Gattino Is Getting Crazy...
draw!(c::AbstractContext, comps::Vector{<:ToolipsSVG.Servable}) style!(con::AbstractContext, spairs::Pair{String, String} ...) style!(con::AbstractContext, s::String, spairs::Pair{String, String} ...) animate!(con::AbstractContext, layer::String, animation::ToolipsSVG.KeyFrames) merge!(c::AbstractContext, c2::AbstractContext) delete_layer!(con::Context, layer::String) rename_layer!(con::Context, layer::String, to::String) move_layer!(con::AbstractContext, layer::String, to::Int64)...
单细胞数据分析新选择（基于Julia编程语言）
作为数据分析语言三剑客之一的Julia，一直以来在生物学领域并没有得到太多的重视。但在数学、物理、化学以及工程计算领域，Julia语言的应用范围一直在不断拓展。2023年Elisabeth Roesch等人在文章Julia for Biologists中为生物学家使用Julia的方方面做了一个较为详尽的概述，从一个宏观的角度推动生物计算开发者尝试这门拥有多种特性的编程语言。 ASCT: Automatic Single-Cell Toolbox 基于对Julia语言的开发兴趣，以及想要建立一个尽量为用户提供自动化参数选择，同时保持灵活性的单细胞数据下游分析工具，西湖大学高性能计算中心开源了一个100%由Julia编写的工具ASCT。该工具目前在github上发布了完全开源的0.9.0版本，并提供了基本分析流程和使用Harmony算法实现的数据整合流程。该工具基本对标R上的Seurat v4版本，并能对不同来源的数据进行一些自适应的参数设置而无需用户自行摸索。本文以其提供的pbmc3k流程来大致介绍该工具的基本使用方法。...
统计学07: F分布与ANOVA
F分布 其中，服从自由度为的卡方分布，服从自由度为的卡方分布 F分布重要用来做Anova，即用方差的思想去做多总体均值的化较 Anova : analysis of variance F值的求法 把 n 组数据放在一起,看成一个总体,算出这个总体的均值2.计算出每组数据的组内平均值3.计算出组间差异 4.计算出组内差异 为每组的平均值 5.计算F值 其中n为组数，m为全部的观测值数 手动ANOVA 设有三台机器，用来生产规格相同的铝合金薄板，取样，测量薄板的厚度。得结果如表。 机器A机器B机器C 0.236 0.257 0.258 0.238 0.253 0.264 0.248 0.255 0.259 0.245 0.254 0.267 0.243 0.261 0.262 Julia代码实现 using DataFrames, CSV,HypothesisTests,Distributions,CairoMakie txt=""" 机器A,机器B,机器C 0.236,0.257,0.258 0.238,0.253,0.264 0.248,0.255,0.259...
A Comprehensive Guide To Common Machine-Learning Preprocessing Techniques
An overview and story of various different preprocessing techniques, as well as where they apply in machine-learning A very common misconception that new and aspiring Data Scientists often have when entering the domain is that most of Data Science revolves around artificial intelligence, machine-learning, and other popular and obscure buzzwords.
Data Engineering in Julia
Image by Author Julia is a natural choice for Data Science and Machine Learning? But what about Data Engineering? In this post, we go over how you can use Julia for various Data Engineering tasks, from the basics to creating pipelines. As you will see, Julia’s performance and ease of use can be leveraged across the entire ML and DS stack with ease.
Revisiting My Meta-Programmed Plotting Library!
Hone.jl A little over a year ago, I started working on a new project in the Julia programming language. The project was meant to serve as mostly practice, as well as proving some concepts and ideas in the language while also performing some experiments with both Julia’s paradigm and methodology. The project was called Hone.jl. Hone.jl is an object-oriented graphing library that uses high-level declarative calls to create meta-code.
NBA Opening Week 2020 Predictions: Applying Julia’s Machine Learning to Sports
2020–21-nba-season-start-date-confirmed-1.jpg With 2020 coming to a close, many people are counting down the minutes until 2021. However, basketball fans are counting down the minutes until the tip off of the new NBA season on December 22. The first game of the opening week will feature Steph Curry and the Golden State Warriors against Kyrie Irving and former Warrior, Kevin Durant, with the Brooklyn Nets.
Building Server in Julia and Connecting to a Database
Join the DZone community and get the full member experience. Join For Free Introduction With the growing popularity of the internet, building a web server has been an absolutely required skillset for any developer. However, there are popular frameworks in some languages available (like Node.js or Django) to build the backend. Still, Julia, being a relatively new language, also provides a framework for the same purpose.
A-Z Julia Endpoint With Genie
An introduction to deploying endpoints with Julia, Genie, and Lathe. (julia logo src = http://julialang.org) notebook Github repo One of the most important tasks that any aspiring Data-Scientist should be able to accomplish is creating and deploying an end-point. End-points are the back-bone of modern machine-learning, and are the easiest way to translate models into a real production environment.
Why Use Julia Programming Language
Explore the advantages of Julia for high-performance programming and data science applications. The framework for autonomous intelligence Julia is designed for high-performance numerical and scientific computing, making it an excellent choice for data engineering tasks that require speed and efficiency. Its just-in-time (JIT) compilation allows for execution speeds comparable to C, which is a significant advantage over interpreted languages like Python.
Julia Programming Examples for AI
Explore practical Julia programming examples that demonstrate AI as a novel programming paradigm, enhancing your coding skills. The framework for autonomous intelligence Julia is designed for high-performance numerical and scientific computing, making it an excellent choice for AI development. Its multiple dispatch feature allows for flexible function definitions, enabling developers to write more generic and reusable code without sacrificing performance.
Julia Programming Language Examples
Explore practical examples of the Julia programming language to enhance your programming skills and understanding. The framework for autonomous intelligence This section explores how Julia is utilized for rapid data science prototyping, highlighting its computational strengths and integration with Python for productionizing results. Restack AI SDK Build autonomous AI products in code, capable of running and persisting month-lasting processes in the background.
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