Search Results for "simple-scan"
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Showing 9 open source projects for "simple-scan"
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AlphaZero.jl
A generic, simple and fast implementation of Deepmind's AlphaZero
...Because AlphaZero is resource-hungry, successful open-source implementations (such as Leela Zero) are written in low-level languages (such as C++) and optimized for highly distributed computing environments. This makes them hardly accessible for students, researchers and hackers. Many simple Python implementations can be found on Github, but none of them is able to beat a reasonable baseline on games such as Othello or Connect Four. As an illustration, the benchmark in the README of the most popular of them only features a random baseline, along with a greedy baseline that does not appear to be significantly stronger. -
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HTTP.jl
HTTP for Julia
...It enables users to send requests, handle responses, and construct REST APIs or web services entirely in Julia. HTTP.jl supports TLS, cookies, headers, streaming, and middleware, making it suitable for both simple scripting and full-scale web service development. -
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Agents.jl
Agent-based modeling framework in Julia
Agents.jl is a pure Julia framework for agent-based modeling (ABM): a computational simulation methodology where autonomous agents react to their environment (including other agents) given a predefined set of rules. The simplicity of Agents.jl is due to the intuitive space-agnostic modeling approach we have implemented: agent actions are specified using generically named functions (such as "move agent" or "find nearby agents") that do not depend on the actual space the agents exist in, nor... -
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PromptingTools.jl
Streamline your life using PromptingTools.jl
...It supports multiple backends, including OpenAI-compatible APIs and local models such as those served through Ollama, allowing users to switch providers without rewriting prompts. The toolkit also includes advanced capabilities such as asynchronous execution, routing, OCR, and experimental agent workflows, making it suitable for both simple and complex AI applications. -
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Vim Codefmt
Vim plugin for syntax-aware code formatting
vim-codefmt is a syntax-aware code formatting plugin for Vim that provides a unified interface to many best-in-class formatters across languages. It exposes simple commands to format either a selected range or an entire buffer, and integrates cleanly into everyday editing workflows. The plugin ships with a registry of built-in formatters and a pluggable architecture, allowing other plugins to register additional formatters without friction. Configuration is handled through maktaba and Glaive flags, so you can choose per-filetype tools, pass custom options, or point to specific formatter executables. ... -
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DecisionTree.jl
Julia implementation of Decision Tree (CART) Random Forest algorithm
Julia implementation of Decision Tree (CART) and Random Forest algorithms. -
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MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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JuliaFEM.jl
The JuliaFEM software library is a framework
The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. ... -
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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... -
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