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Source channel @githubtrending · Post #14965 · Jul 16
#rust#agent#ai#amazon_q#cli#linux#llm#macos#mcp#open_source#productivity#rust#shell#terminal#typescript Amazon Q CLI is a powerful tool that lets you interact with AWS and your development environment using natural language right from your terminal. It helps you write code, run commands, and manage AWS resources faster by understanding your context and providing smart suggestions, autocompletion, and even translating plain English into shell commands. It supports multi-turn conversations, so you can ask follow-up questions and get real-time help without leaving the command line. This boosts your productivity by simplifying complex tasks, reducing errors, and speeding up development workflows, making it easier to manage projects and infrastructure efficiently[1][2][3]. https://github.com/aws/amazon-q-developer-cli
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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