Search Results for "use" - Page 6
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Showing 584 open source projects for "use"
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Turn Your Content into Interactive Magic - For FreeTransform anything you share into an engaging digital experience - for free. Instantly convert your PDFs, slides, and files into dynamic, interactive sessions with built-in collaboration tools, activities, and real-time assessment. From teaching to training to team building, make every presentation unforgettable. Used by millions for education, business, and professional development.
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime... -
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Bayesian Optimization
Python implementation of global optimization with gaussian processes
... tour notebook to learn how to use the package's most important features. Take a look at the advanced tour notebook to learn how to make the package more flexible, how to deal with categorical parameters, how to use observers, and more. Explore the options exemplifying the balance between exploration and exploitation and how to control it. Explore the domain reduction notebook to learn more about how search can be sped up by dynamically changing parameters' bounds. -
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GoJS
JavaScript diagramming library for interactive flowcharts & org charts
... for viewing or editing. Automatic layouts make different visualization options easy. Dynamically add ports and custom link routing. Use data-bindings to save and load routes within the Model JSON. Use groups as containers and subgraphs, with group members bound by their own rules and layouts. Model industrial processes, workflows, SCADA diagrams and more. -
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visx
Visualization components
visx a collection of expressive, low-level visualization primitives for React. At Airbnb, we made it a goal to unify our visualization stack across the company and in the process, we created a new project that brings together the power of D3 with the joy of React. visx is split into multiple packages. Start small and use only what you need. Bring your own state management, animation library, or CSS-in-JS solution. Odds are good your React app already has an opinion on how animation, theming... -
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The Julia Programming Language
High-level, high-performance dynamic language for technical computing
Julia is a fast, open source high-performance dynamic language for technical computing. It can be used for data visualization and plotting, deep learning, machine learning, scientific computing, parallel computing and so much more. Having a high level syntax, Julia is easy to use for programmers of every level and background. Julia has more than 2,800 community-registered packages including various mathematical libraries, data manipulation tools, and packages for general purpose computing... -
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DataChain
AI-data warehouse to enrich, transform and analyze unstructured data
Datachain enables multimodal API calls and local AI inferences to run in parallel over many samples as chained operations. The resulting datasets can be saved, versioned, and sent directly to PyTorch and TensorFlow for training. Datachain can persist features of Python objects returned by AI models, and enables vectorized analytical operations over them. The typical use cases are data curation, LLM analytics and validation, image segmentation, pose detection, and GenAI alignment. Datachain... -
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RevoGrid
Powerful virtual data grid smartsheet with advanced customization
Support Millions of cells and thousands of columns easy and efficiently for fast data rendering. Easy to use. For large organizations managing massive datasets. Performance and scalability to handle even the most complex data tables. Rapid prototyping with intuitive and extendable codebase. Agility to build robust data-driven applications in no time. Quickly build elegant and efficient data grids. Lightweight yet powerful architecture lets you easily scale as your needs grow. -
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DynamicalBilliards.jl
An easy-to-use, modular, extendable and absurdly fast Julia package
A Julia package for dynamical billiard systems in two dimensions. The goals of the package is to provide a flexible and intuitive framework for fast implementation of billiard systems of arbitrary construction. -
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Vulkan.jl
Using Vulkan from Julia
Vulkan.jl is a lightweight wrapper around the Vulkan graphics and compute library. It exposes abstractions over the underlying C interface, primarily geared toward developers looking for a more natural way to work with Vulkan with minimal overhead. It builds upon the core API provided by VulkanCore.jl. Because Vulkan is originally a C specification, interfacing with it requires some knowledge before correctly being used from Julia. This package acts as an abstraction layer, so that you don't... -
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DiffOpt.jl
Differentiating convex optimization programs w.r.t. program parameters
DiffOpt.jl is a package for differentiating convex optimization programs (JuMP.jl or MathOptInterface.jl models) with respect to program parameters. Note that this package does not contain any solver. This package has two major backends, available via the reverse_differentiate! and forward_differentiate! methods, to differentiate models (quadratic or conic) with optimal solutions. Differentiable optimization is a promising field of convex optimization and has many potential applications in... -
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Gaius.jl
Divide and Conquer Linear Algebra
Gaius.jl is a multi-threaded BLAS-like library using a divide-and-conquer strategy to parallelism, and built on top of the fantastic LoopVectorization.jl. Gaius spawns threads using Julia's depth-first parallel task runtime and so Gaius's routines may be fearlessly nested inside multi-threaded Julia programs. Gaius is not stable or well-tested. Only use it if you're adventurous. -
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LLVM.jl
Julia wrapper for the LLVM C API
A Julia wrapper for the LLVM C API. The LLVM.jl package is a Julia wrapper for the LLVM C API, and can be used to work with the LLVM compiler framework from Julia. You can use the package to work with LLVM code generated by Julia, to interoperate with the Julia compiler, or to create your own compiler. It is heavily used by the different GPU compilers for the Julia programming language. -
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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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CBinding.jl
Automatic C interfacing for Julia
Use CBinding.jl to automatically create C library bindings with Julia at runtime. In order to support the fully automatic conversion and avoid name collisions, the names of C types or functions are mangled a bit to work in Julia. Therefore everything generated by CBinding.jl can be accessed with the c"..." string macro to indicate that it lives in C-land. As an example, the function func above is available in Julia as c"func". It is possible to store the generated bindings to more user-friendly... -
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JuliaWorkshop
Intensive Julia workshop that takes you from zero to hero
... be able to use it like a pro. The material has been updated during July-December 2023 to Julia v1.9+ and the corresponding latest stable versions of used packages -
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Coverage.jl
Take Julia code coverage and memory allocation results, do useful thin
Julia can track how many times, if any, each line of your code is run. This is useful for measuring how much of your code base your tests actually test, and can reveal the parts of your code that are not tested and might be hiding a bug. You can use Coverage.jl to summarize the results of this tracking or to send them to a service like Coveralls.io or Codecov.io. Julia can track how much memory is allocated by each line of your code. This can reveal problems like type instability, or operations... -
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BlockArrays.jl
BlockArrays for Julia
... BlockArray stores each block contiguously while the type PseudoBlockArray stores the full matrix contiguously. This means that BlockArray supports fast noncopying extraction and insertion of blocks while PseudoBlockArray supports fast access to the full matrix to use in for example a linear solver. -
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LaTeXStrings.jl
convenient input and display of LaTeX equation strings for Julia
This is a small package to make it easier to type LaTeX equations in string literals in the Julia language, written by Steven G. Johnson. With ordinary strings in Julia, to enter a string literal with embedded LaTeX equations you need to manually escape all backslashes and dollar signs: for example, $\alpha^2$ is written \$\\alpha^2\$. Also, even though IJulia is capable of displaying formatted LaTeX equations (via MathJax), an ordinary string will not exploit this. -
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jluna
Julia Wrapper for C++ with Focus on Safety, Elegance, and Ease of Use
Julia is a beautiful language, it is well-designed and well-documented. Julia's C-API is also well-designed, less beautiful, and much less... documented. jluna aims to fully wrap the official Julia C-API, replacing it in projects with C++ as the host language, by making accessing Julia's unique strengths through C++ safe, hassle-free, and just as beautiful. It allows for easy integration of Julia scripts and packages into projects with C++ as the host language, making language interaction... -
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DynamicHMC
Implementation of robust dynamic Hamiltonian Monte Carlo methods
... of coding a (log) posterior density in Julia. This approach allows the use of standard tools like profiling and benchmarking to optimize its performance. -
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JuliaSyntax
A Julia frontend, written in Julia
... various use cases. Parsing to the standard Expr AST is always possible and will be stable. The intention is to extend this library over time to cover more of the Julia compiler front end. -
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ComponentArrays.jl
Arrays with arbitrarily nested named components
... are for use in DifferentialEquations.jl and Optim.jl, but anything that requires flat vectors is fair game. -
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FiniteDifferences.jl
High accuracy derivatives, estimated via numerical finite differences
FiniteDifferences.jl estimates derivatives with finite differences. See also the Python package FDM. FiniteDiff.jl and FiniteDifferences.jl are similar libraries: both calculate approximate derivatives numerically. You should definitely use one or the other, rather than the legacy Calculus.jl finite differencing, or reimplementing it yourself. At some point in the future, they might merge, or one might depend on the other. -
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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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Mousetrap.jl
Finally, a GUI Engine made for Julia
Mousetrap is a GUI library designed for Julia. It fully wraps GTK4 (which is written in C), vastly simplifying its interface to improve ease of use without sacrificing flexibility. It aims to give developers of all skill levels the tools to start creating complex GUI applications with little time and effort while taking full advantage of Julia's idiosyncrasies.