CPU and GPU-accelerated Machine Learning Library
biddata/bidmach 的主要功能包括：Machine Learning, 机器学习框架, Data Analysis and Visualization。
biddata/bidmach 的开源替代品包括： apache/spark — Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation… azure/mmlspark — Mmlspark is a distributed framework for executing machine learning models, data transformations, and AI service… ai4finance-foundation/finrl — FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated… alexrudall/ruby-openai — OpenAI API + Ruby! 🤖❤️ GPT-5 & Realtime WebRTC compatible! aksnzhy/xlearn — High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization… biddata/bidmat — A CPU and GPU-accelerated matrix library for data mining.
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.
OpenAI API Ruby! 🤖❤️ GPT-5 & Realtime WebRTC compatible!
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
Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation graphs. It functions as a distributed machine learning framework, a graph processing system, a real-time stream processor, and a SQL analytics engine. The system enables the execution of distributed SQL querying, large-scale graph analysis, and real-time stream analytics across clusters of machines. It also provides a scalable environment for implementing machine learning algorithms and predictive model development on massive datasets. The engine incorporates relational query e