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Showing 11 open source projects for "code blocks sample"
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Franklin.jl
Static site generator. Simple, customisable, fast, maths with KaTeX
Franklin is a simple static site generator (SSG) oriented towards technical blogging (code, maths, ...), flexibility and extensibility. The base syntax is plain markdown with a few extensions such as the ability to define and use LaTeX-like commands in or outside of maths environments and the possibility to evaluate code blocks on the fly. -
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Pluto.jl
Simple reactive notebooks for Julia plutojl.org
We are on a mission to make scientific computing more accessible and fun. Writing a notebook is not just about writing the final document, Pluto empowers the experiments and discoveries that are essential to getting there. -
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TimerOutputs.jl
Formatted output of timed sections in Julia
TimerOutputs.jl is a lightweight Julia package that provides a structured way to measure and report the execution time of different parts of code. It is particularly useful for performance profiling in scientific computing, allowing developers to annotate sections of code and generate readable timing summaries. TimerOutputs.jl supports nested timers and formatted output to both terminal and files, helping users easily identify bottlenecks in their programs. -
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Documenter.jl
A documentation generator for Julia
A documentation generator for Julia. A package for building documentation from docstrings and markdown files. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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OhMyREPL.jl
Syntax highlighting and other enhancements for the Julia REPL
OhMyREPL.jl is a Julia package that enhances the Julia REPL (Read-Eval-Print Loop) experience with syntax highlighting, bracket matching, prompt customization, and automatic indentation. It is designed to make the command-line interface more visually appealing and user-friendly, especially during interactive development and debugging. It runs entirely in the terminal and does not require external dependencies or GUI. -
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Gen.jl
A general-purpose probabilistic programming system
An open-source stack for generative modeling and probabilistic inference. Gen’s inference library gives users building blocks for writing efficient probabilistic inference algorithms that are tailored to their models, while automating the tricky math and the low-level implementation details. Gen helps users write hybrid algorithms that combine neural networks, variational inference, sequential Monte Carlo samplers, and Markov chain Monte Carlo. Gen features an easy-to-use modeling language for writing down generative models, inference models, variational families, and proposal distributions using ordinary code. -
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BenchmarkTools.jl
A benchmarking framework for the Julia language
BenchmarkTools makes performance tracking of Julia code easy by supplying a framework for writing and running groups of benchmarks as well as comparing benchmark results. This package is used to write and run the benchmarks found in BaseBenchmarks.jl. The CI infrastructure for automated performance testing of the Julia language is not in this package but can be found in Nanosoldier.jl. Our story begins with two packages, "Benchmarks" and "BenchmarkTrackers". The Benchmarks package... -
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DynamicHMC
Implementation of robust dynamic Hamiltonian Monte Carlo methods
Implementation of robust dynamic Hamiltonian Monte Carlo methods in Julia. In contrast to frameworks that utilize a directed acyclic graph to build a posterior for a Bayesian model from small components, this package requires that you code a log-density function of the posterior in Julia. Derivatives can be provided manually, or using automatic differentiation. Consequently, this package requires that the user is comfortable with the basics of the theory of Bayesian inference, to the extent... -
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Augmentor.jl
A fast image augmentation library in Julia for machine learning
...Augmentor is a real-time image augmentation library designed to render the process of artificial dataset enlargement more convenient, less error prone, and easier to reproduce. It offers the user the ability to build a stochastic image-processing pipeline (or simply augmentation pipeline) using image operations as building blocks. In other words, an augmentation pipeline is little more but a sequence of operations for which the parameters can (but need not) be random variables, as the following code snippet demonstrates. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Nonlinear Dynamics
A concise introduction interlaced with code
This repository holds material related with the textbook Nonlinear Dynamics: A Concise Introduction Interlaced with code, co-authored by George Datseris and Ulrich Parlitz. The textbook will be published by Springer-Nature, in the series Undergraduate Lecture Notes in Physics. -
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Stats With Julia Book
Collection of runnable Julia code examples for a statistics book
StatsWithJuliaBook is the companion code repository for the book Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. It contains over 200 code blocks that correspond to the book’s ten chapters and three appendices, covering topics from probability theory and data summarization to regression analysis, hypothesis testing, and machine learning basics.
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