Showing 112 open source projects for "no code"

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  • 1
    Starlight.jl

    Starlight.jl

    A greedy game engine for greedy programmers

    ...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". However, Starlight is most fundamentally a scripting layer for SDL, Vulkan, and Bullet (via the Telescope backend), meaning it can be used for any application that needs high-performance rendering and physics. ...
    Downloads: 0 This Week
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  • 2
    Nonlinear Dynamics

    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.
    Downloads: 0 This Week
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  • 3
    ReinforcementLearningAnIntroduction.jl

    ReinforcementLearningAnIntroduction.jl

    Julia code for the book Reinforcement Learning An Introduction

    This project provides the Julia code to generate figures in the book Reinforcement Learning: An Introduction(2nd). One of our main goals is to help users understand the basic concepts of reinforcement learning from an engineer's perspective. Once you have grasped how different components are organized, you're ready to explore a wide variety of modern deep reinforcement learning algorithms in ReinforcementLearningZoo.jl.
    Downloads: 8 This Week
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  • 4
    ReplMaker.jl

    ReplMaker.jl

    Simple API for building repl modes in Julia

    The idea behind ReplMaker.jl is to make a tool for building (domain-specific) languages in Julia. Suppose you've invented some language called MyLang and you've implemented a parser that turns MyLang code into Julia code which is then supposed to be executed by the Julia runtime. With ReplMaker.jl, you can simply hook your parser into the package and ReplMaker will then create a REPL mode where end users just type MyLang code and have it executed automatically. My hope is for this to be useful to someone who implements a full language or DSL in Julia that uses syntax not supported by Julia's parser and doesn't want to deal with the headache of making their own REPL mode.
    Downloads: 4 This Week
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  • 5
    Stats With Julia Book

    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.
    Downloads: 2 This Week
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  • 6
    JuliaDB.jl

    JuliaDB.jl

    Parallel analytical database in pure Julia

    ...JuliaDB provides distributed table and array datastructures with convenient functions to load data from CSV. JuliaDB is Julia all the way down. This means queries can be composed with Julia code that may use a vast ecosystem of packages.
    Downloads: 3 This Week
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  • 7
    ArgCheck.jl

    ArgCheck.jl

    Package for checking function arguments

    Package for checking function arguments. @argcheck code is as fast as @assert or a hand written if. That being said it is possible to erase argchecks, much like one can erase bounds checking using @inbounds. This feature is currently experimental. It may be silently changed or removed without increasing the major ArgCheck version number.
    Downloads: 8 This Week
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  • 8
    CuArrays.jl

    CuArrays.jl

    A Curious Cumulation of CUDA Cuisine

    CuArrays provides a fully-functional GPU array, which can give significant speedups over normal arrays without code changes. CuArrays are implemented fully in Julia, making the implementation elegant and extremely generic.
    Downloads: 10 This Week
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  • 9
    julia-vim

    julia-vim

    Vim support for Julia.

    Vim support for Julia. This plug-in adds some functionality to substitute LaTeX code sequences (e.g. \alpha) with corresponding Unicode symbols (e.g. α). By default, these substitutions must be triggered explicitly by pressing the Tab key, as in the Julia command line (the REPL); however, an automatic, as-you-type mode can also be activated, and a method based on keymap is also available. This feature also works in command mode, e.g. when searching the files with the / or ?
    Downloads: 0 This Week
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  • 10
    BERT

    BERT

    Connector for Excel and the programming languages R and Julia

    ...Everything else – loading the function into Excel, managing parameters, and handling type conversion – is done automatically for you. It really could not be any easier. BERT also has a console that you can use to control Excel in real time, right from your R code. And (if you want), you can call R functions from VBA as well.
    Downloads: 0 This Week
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  • 11
    Mocha.jl

    Mocha.jl

    Deep Learning framework for Julia

    Mocha.jl is a deep learning framework for Julia, inspired by the C++ Caffe framework. It offers efficient implementations of gradient descent solvers and common neural network layers, supports optional unsupervised pre-training, and allows switching to a GPU backend for accelerated performance. The development of Mocha.jl happens in relative early days of Julia. Now that both Julia and the ecosystem has evolved significantly, and with some exciting new tech such as writing GPU kernels...
    Downloads: 0 This Week
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  • 12
    ParallelAccelerator.jl

    ParallelAccelerator.jl

    ParallelAccelerator package, part of the High Performance Scripting

    ParallelAccelerator is a Julia package for speeding up compute-intensive Julia programs. In particular, Julia code that makes heavy use of high-level array operations is a good candidate for speeding up with ParallelAccelerator. With the @acc macro that ParallelAccelerator provides, users may specify parts of a program to accelerate. ParallelAccelerator compiles these parts of the program to fast native code. It automatically eliminates overheads such as array bounds checking when it is safe to do so. ...
    Downloads: 0 This Week
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