A simple machine learning framework written in Swift 🤖
The main features of somnibyte/mlkit are: Machine Learning Frameworks.
Open-source alternatives to somnibyte/mlkit include: airbnb/aerosolve — Aerosolve is a machine learning framework designed for training and deploying interpretable models. It functions as a… aksnzhy/xlearn — High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization… alan-turing-institute/mlj.jl — A Julia machine learning framework. alejandro-isaza/braincore — The iOS and OS X neural network framework. alexrudall/ruby-openai — OpenAI API + Ruby! 🤖❤️ GPT-5 & Realtime WebRTC compatible! ai4finance-foundation/finrl — FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated…
Aerosolve is a machine learning framework designed for training and deploying interpretable models. It functions as a feature engineering tool and a model trainer that utilizes sparse feature modeling to simplify weight debugging and accelerate data iteration. The system includes a specialized domain-specific transformation language for converting raw data families into model-ready representations. It also provides capabilities for visual content analysis by mapping images into dense high-dimensional vector spaces to rank and organize data by style or content. The framework allows for human-
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
A Julia machine learning framework
FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated trading strategies. It functions as a quantitative finance toolkit that integrates deep learning algorithms with financial market simulations to address complex portfolio management and asset allocation tasks. The platform provides an end-to-end pipeline for transforming raw market data into actionable trading models. The project distinguishes itself through a layered, modular architecture that separates data processing, environment simulation, and agent training. This design allow