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Source channel @githubtrending · Post #15085 · Aug 22
#python#chatbi#deepseek#llm#nl2sql#rag#sqlbot#text_to_sql#text2sql SQLBot is an easy-to-use intelligent system that turns natural language questions into SQL queries using advanced AI models and retrieval-augmented generation (RAG). You just need to set up your AI model and data source to start asking questions about your data. It integrates smoothly with other business systems and AI platforms, making it simple to add smart data querying to your apps. It also ensures data security with workspace-based resource isolation and fine-grained access control. You can quickly deploy it on a Linux server using Docker, enabling fast, secure, and intelligent data interaction without needing deep SQL knowledge. This saves you time and improves data accessibility. https://github.com/dataease/SQLBot
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@repo_science · Post #3807 · 12/19/2023, 05:08 AM
#AutoML 🐍 AutoML: Build Production-Ready Models Quickly! Learn the basics of building production-ready automated machine learning (AutoML) models. ----- Main channel: @repo_science Coupons: @freecoupons_reposcience -----
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@githubtrending · Post #14863 · 06/24/2025, 01:30 PM
#other#automl#chatgpt#data_analysis#data_science#data_visualization#data_visualizations#deep_learning#gpt#gpt_3#jax#keras#machine_learning#ml#nlp#python#pytorch#scikit_learn#tensorflow#transformer This is a comprehensive, regularly updated list of 920 top open-source Python machine learning libraries, organized into 34 categories like frameworks, data visualization, NLP, image processing, and more. Each project is ranked by quality using GitHub and package manager metrics, helping you find the best tools for your needs. Popular libraries like TensorFlow, PyTorch, scikit-learn, and Hugging Face transformers are included, along with specialized ones for time series, reinforcement learning, and model interpretability. This resource saves you time by guiding you to high-quality, actively maintained libraries for building, optimizing, and deploying machine learning models efficiently. https://github.com/ml-tooling/best-of-ml-python