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KernelDensity.jl
Kernel density estimators for Julia
Kernel density estimators for Julia. -
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ForwardDiff.jl
Forward Mode Automatic Differentiation for Julia
ForwardDiff implements methods to take derivatives, gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really) using forward mode automatic differentiation (AD). While performance can vary depending on the functions you evaluate, the algorithms implemented by ForwardDiff generally outperform non-AD algorithms (such as finite-differencing) in both speed and accuracy. Functions like f which map a vector to a scalar are the best case... -
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PlutoSliderServer.jl
Web server to run just the `@bind` parts of a Pluto.jl notebook
Web server to run just the @bind parts of a Pluto.jl notebook. PlutoSliderServer can run a notebook and generate the export HTML file. This will give you the same file as the export button inside Pluto (top right), but automatically, without opening a browser. One use case is to automatically create a GitHub Pages site from a repository with notebooks. For this, take a look at our template repository that used GitHub Actions and PlutoSliderServer to generate a website on every commit. Many... -
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HomotopyContinuation.jl
A Julia package for solving systems of polynomials
HomotopyContinuation.jl is a Julia package for solving systems of polynomial equations by numerical homotopy continuation. Many models in the sciences and engineering are expressed as sets of real solutions to systems of polynomial equations. We can optimize any objective whose gradient is an algebraic function using homotopy methods by computing all critical points of the objective function. An important special case is when the objective function is the euclidean distance to a given point... -
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Surrogates.jl
Surrogate modeling and optimization for scientific machine learning
A surrogate model is an approximation method that mimics the behavior of a computationally expensive simulation. In more mathematical terms: suppose we are attempting to optimize a function f(p), but each calculation of f is very expensive. It may be the case we need to solve a PDE for each point or use advanced numerical linear algebra machinery, which is usually costly. The idea is then to develop a surrogate model g which approximates f by training on previous data collected from evaluations... -
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TensorBoardLogger.jl
Easy peasy logging to TensorBoard with Julia
... the behaviour in case there already exists a document at the given path. -
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LossFunctions.jl
Julia package of loss functions for machine learning
... of carefully implemented loss functions, as well as an API to query their properties (e.g. convexity). Furthermore, we expose methods to compute their values, derivatives, and second derivatives for single observations as well as arbitrarily sized arrays of observations. In the case of arrays a user additionally has the ability to define if and how element-wise results are averaged or summed over. -
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jlrs
Julia bindings for Rust
... with Julia 1.6 up to and including Julia 1.10. The JlrsCore package must also have been installed, if this is not the case it will automatically be added when jlrs is initialized by default. jlrs has not been tested with juliaup yet on Linux and macOS. -
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Starlight.jl
A greedy game engine for greedy programmers
Welcome to the documentation for Starlight.jl, a greedy application framework for greedy developers. Its primary use case is video games, but the power of Julia, SDL2, Vulkan, and the Bullet Physics SDK can be leveraged to make just about anything you want. With a focus on flexibility and code quality, Starlight aims to be such a framework. It includes a suite of components and integrations that make it particuarly well-suited for video games, so it is not a stretch to call it a "game engine... -
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