#Julia

Flux presents a straightforward method for machine learning using a pure-Julia framework. Its efficient abstractions take advantage of Julia's native GPU and automatic differentiation features, facilitating the handling of complex tasks. Compatible with Julia 1.9 or newer versions, it offers simple setup and detailed documentation to support experimentation. A valuable tool for researchers and developers in the Julia ecosystem seeking a versatile machine learning framework.

Agents.jl is a high-performance Julia framework designed for agent-based modeling, providing a swift and user-friendly approach to computational simulations. It offers superior performance compared to platforms such as MASON, NetLogo, and Mesa, standing out for its minimal coding requirements and straightforward learning path. The framework features a comprehensive interface with numerous agent actions and Open Street Maps compatibility, supporting both discrete-time and continuous-time simulations. Explore detailed features and documentation through its online resources.