Popular GitHub Repositories for Combine

Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.

A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch

Superlinked is a Python framework for AI Engineers building high-performance search & recommendation applications that combine structured and unstructured data.

A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021 (Bianchi et al.).

deployKF builds machine learning platforms on Kubernetes. We combine the best of Kubeflow, Airflow?, and MLflow? into a complete platform.

BIDScoin converts your source-level neuroimaging data to BIDS

拼好RAG：手搓并融合了GraphRAG、LightRAG、Neo4j-llm-graph-builder进行知识图谱构建以及搜索；整合DeepSearch技术实现私域RAG的推理；自制针对GraphRAG的评估框架| Integrate GraphRAG, LightRAG, and Neo4j-llm-graph-builder for knowledge graph construction and search. Combine DeepSearch for private RAG reasoning. Create a custom evaluation framework for GraphRAG.

Real-time Intrusion Detection System implementing Machine Learning. We combine Supervised Learning (RF) for detecting known attacks from CICIDS 2018 & SCVIC-APT datasets, and Unsupervised Learning (AE) for anomaly detection.

Multi-Input Deep Neural Networks with PyTorch-Lightning - Combine Image and Tabular Data

Do you like n8n? Do you like Laravel? Let's combine them and make l8n!

A practical example on how to combine both a CNN and a RNN to classify images.

combine neural nets and cellular automata

Pipeline LLMs offline combine with own context for generating answer

Images and Videos, Real-time Facial Expession Recognition Application with Combine CNN , deep learning features extraction incorporate SIFT, FAST feature .

Get ready to embark on an exciting journey as we combine the power of Amazon Bedrock, ReactJS and the AWS JavaScript SDK to create a generative AI application with minimal integration code.

This project aims to combine the latest LLMs, Multi-Step Asynchronous Function Calling, Natural Language Processing, and Text-to-Speech.

Combine Spark and Python to process large datasets and unlock the power of parallel computing and machine learning

A Hybrid recommendation engine built on deep learning architecture, which has the potential to combine content-based and collaborative filtering recommendation mechanisms using a deep learning supervisor

EmoInt provides a high level wrapper to combine various word embeddings and creating ensembles from multiple trained models