An 18-lesson beginner course with code samples on building AI agents using Microsoft's agent frameworks and services.
Inference code, checkpoints, and notebooks for Meta's Segment Anything Model (SAM), which produces object masks from points, boxes, or automatically for whole images.
Meta's Segment Anything Model 2 for promptable segmentation in images and videos, with inference code, checkpoints, and example notebooks.
Sample notebooks, code, and apps demonstrating how to build with generative AI models on Google Cloud.
GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.
Jupyter notebooks and code samples accompanying Wes McKinney's book on data analysis with Python, pandas, and NumPy.
Samples using the Agent2Agent (A2A) Protocol
A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.
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Learn quantitative finance with this comprehensive lecture series. Adapted from the Quantopian Lecture Series. Uses free sample data.
Official community-driven Azure AI Examples
This is a workshop designed for Amazon Bedrock a foundational model service.
Segment Anything for Microscopy
Samples and data for Microsoft Fabric Learn content
Code samples used on cloud.google.com
MongoDB's Generative AI Showcase: an exhaustive collection of examples and sample applications covering Retrieval-Augmented Generation (RAG), AI agents, and industry-specific use cases.
This is a Phi Family of SLMs book for getting started with Phi Models. Phi a family of open sourced AI models developed by Microsoft. Phi models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks
SMT: The Surrogate Modeling Toolbox
A repository of code samples for Vector search capabilities in Azure AI Search.
Notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage machine learning and generative AI workflows using Google Cloud Vertex AI.
Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.
Fantasy Premier League Kotlin/Compose Multiplatform sample
組織横断的にチームを組成し、機械学習による成長サイクルを実現する計画を立てるワークショップ
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End-to-end SFM framework based on GTSAM
Códigos, notebooks e materiais da 1ª Escola de Processamento de Linguagem Natural - EPLN 2026.
Notebooks, code samples and reference for machine learning and generative ai on Google Cloud for the Advanced Solutions Lab (ASL) bootcamps.
SAMS - Scalable Agentic Market Simulator, a next gen AI finance automation paradigm, suitable for high frequency scenario while built for continuous learning and more sophisticated market simulation.
A collection of Jupyter notebook exercises with instructions and solutions for practicing pandas skills by topic.
This repository contains examples for customers to get started using the Amazon Bedrock Service. This contains examples for all available foundational models