DataTalksClub / mlops-zoomcamp
Free MLOps course from DataTalks.Club
See what the GitHub community is most excited about today.
Free MLOps course from DataTalks.Club
Free Data Engineering course!
LLM-PowerHouse: Unleash LLMs' potential through curated tutorials, best practices, and ready-to-use code for custom training and inferencing.
Lab Materials for MIT 6.S191: Introduction to Deep Learning
Python toolkit for quantitative finance
An open source implementation of CLIP.
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
Learn ML engineering for free in 4 months!
Scripts for fine-tuning Meta Llama3 with composable FSDP & PEFT methods to cover single/multi-node GPUs. Supports default & custom datasets for applications such as summarization and Q&A. Supporting a number of candid inference solutions such as HF TGI, VLLM for local or cloud deployment. Demo apps to showcase Meta Llama3 for WhatsApp & Messenger.
PyTorch code and models for the DINOv2 self-supervised learning method.
A game theoretic approach to explain the output of any machine learning model.
AI Observability & Evaluation
Unveiling the Hidden Layers of the Web – A Comprehensive Web Reconnaissance Tool
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
This repository contains a comprehensive computer vision/machine learning football project that uses YOLO for object detection, Kmeans for pixel segmentation, optical flow for motion tracking, and perspective transformation to analyze player movements in football videos
ML-capsule is a Project for beginners and experienced data science Enthusiasts who don't have a mentor or guidance and wish to learn Machine learning. Using our repo they can learn ML, DL, and many related technologies with different real-world projects and become Interview ready.
Google Research
Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Official code for "FeatUp: A Model-Agnostic Frameworkfor Features at Any Resolution" ICLR 2024
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Multiple NVIDIA GPUs or Apple Silicon for Large Language Model Inference?