Search Results for "ai course experiments"
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Showing 175 open source projects for "ai course experiments"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Codebase to Course
A Claude Code skill that turns any codebase into an HTML course
Codebase to Course is an AI-powered development tool that converts any software repository into a fully interactive educational experience presented as a self-contained HTML course. It is implemented as a skill for Claude Code and is designed to help users understand how a codebase works without requiring a formal computer science background. The tool analyzes the structure and behavior of a project and generates a visually rich, scroll-based course that includes diagrams, animations, and contextual explanations. ... -
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ai-notebooks
Some ipython notebooks implementing AI algorithms
ai-notebooks is a collection of Jupyter notebooks that implements machine-learning and artificial-intelligence ideas in compact, inspectable experiments. The examples are written primarily in Python and use frameworks including TensorFlow, PyTorch, Keras, JAX, and tinygrad. Projects explore problems such as MNIST learning, GANs, VAEs, model compression, and knowledge distillation. -
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Generative AI for Beginners .NET
Hands-on .NET course for building real-world generative AI apps
Generative AI for Beginners .NET is a hands-on course that helps developers build real-world AI applications using the .NET ecosystem. It walks through core concepts such as text generation, chat-based interactions, and integrating large language models into applications. Each lesson includes short videos, working code samples, and step-by-step instructions, making it easy to follow and apply immediately. -
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LLM Course
Course to get into Large Language Models (LLMs)
LLM Course is a hands-on, notebook-driven path for learning how large language models work in practice, from data curation to training, fine-tuning, evaluating, and deploying. It emphasizes reproducible experiments: each step is demonstrated with runnable code, clear dependencies, and references to commonly used open-source models and libraries. -
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AI Infra
Understanding of AI Infra: Quantitative Analysis and System Design
AI Infra Book is an open-source technical book and companion repository focused on the infrastructure behind modern large language models. It approaches inference and training through quantitative analysis of hardware limits, data movement, model architecture, and distributed systems. The book contains twelve chapters supported by formulas, diagrams, experiments, and case studies. -
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second-brain-ai-assistant-course
Learn to build your Second Brain AI assistant with LLMs
The Second Brain AI Assistant Course is an open-source educational project designed to teach developers how to build a personal AI assistant that interacts with a user’s knowledge base. The course provides a structured curriculum that walks learners through the architecture and implementation of a production-ready AI system powered by large language models. -
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esp32-ai
Running a 28.9M parameter LLM on an $8 microcontroller
...Trained on TinyStories, the model produces short, simple stories rather than answering questions, following instructions, or providing factual knowledge. The repository includes firmware, wiring and flashing instructions, training code, quantization experiments, ablations, and measured results. -
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AI Agent Book
Deep Understanding AI Agents
AI Agent Book is an open educational repository for Understanding AI Agents: Design Principles and Engineering Practice. It explains agents through the formula of a language model combined with context and tools. Ten chapters move from core concepts to context engineering, memory, RAG, knowledge graphs, MCP tools, and coding agents. Later material covers evaluation, supervised fine-tuning, reinforcement learning, self-improvement, multimodal interaction, robotics, and multi-agent cooperation. ... -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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AI Notes
Curated AI engineering notes on LLMs, generative models, and tools
...These notes include observations, references, experiments, and summaries of important research and industry developments in AI. ai-notes also contains collections of prompts, curated learning materials, and categorized resources intended to help developers explore AI capabilities and practical applications. -
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AI-Researcher
AI-Researcher: Autonomous Scientific Innovation
...The system integrates retrieval mechanisms to pull in external knowledge sources, contextually analyze documents and papers, and build structured representations of ideas and arguments that can later be turned into coherent reports or drafts. Rather than simply generating text from prompts, AI-Researcher orchestrates sequences of subtasks — such as extracting definitions, identifying key experiments, and tracking citations — and uses self-refinement loops to iteratively improve outputs. -
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AI Researcher
An autonomous AI researcher
AI Researcher is an experimental open-source project that demonstrates how multiple AI agents can collaborate to conduct complex research tasks from start to finish with minimal human intervention. It orchestrates agents that can generate research questions, perform literature reviews, execute experiments, analyze results, and synthesize findings into structured outputs like reports or code. -
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Generative AI for Beginners (Version 3)
21 Lessons, Get Started Building with Generative AI
Generative AI for Beginners is a 21-lesson course by Microsoft Cloud Advocates that teaches the fundamentals of building generative AI applications in a practical, project-oriented way. Lessons are split into “Learn” modules for core concepts and “Build” modules with hands-on code in Python and TypeScript, so you can jump in at any point that matches your goals. -
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AI_Tutorial
A selection of learning materials, search, recommendation, advertising
...The project functions as a centralized knowledge base designed to help engineers and researchers discover tutorials, technical articles, algorithm explanations, and architecture discussions from across the AI ecosystem. Rather than focusing on a single framework or course, the repository collects materials from many sources such as open-source projects, technical blogs, research papers, and industry engineering posts. The curated content includes topics like recommendation systems, search engine architecture, neural networks, graph neural networks, and modern deep learning techniques. ... -
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The AI Scientist-v2
Workshop-Level Automated Scientific Discovery via Agentic Tree Search
AI-Scientist-v2 is an advanced autonomous research system designed to perform end-to-end scientific discovery using large language models and agent-based orchestration. The platform is capable of generating original research ideas, designing and executing experiments, analyzing and visualizing results, and producing full academic papers without direct human intervention. -
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AI-Job-Notes
AI algorithm position job search strategy
AI-Job-Notes is a pragmatic notebook for landing roles in machine learning, computer vision, and related engineering tracks. It assembles study paths, checklists, and interview prep materials, but also covers job-search mechanics—portfolio building, resume patterns, and communication tips. The emphasis is on doing: practicing with project ideas, setting up reproducible experiments, and showcasing results that convey impact. -
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AI Engineering Hub
In-depth tutorials on LLMs, RAGs and real-world AI agent applications
The AI Engineering Hub repository is a large open-source collection of hands-on projects, tutorials, and real-world AI engineering resources designed to help developers learn and build with modern AI technologies, especially large language models (LLMs), retrieval-augmented generation (RAG), and agent-based systems. It includes more than 90 production-ready projects across skill levels, organized into beginner, intermediate, and advanced categories to guide users progressively from simple experiments to complex AI workflows. ... -
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OptScale
FinOps and MLOps platform to run ML/AI and regular cloud workloads
Run ML/AI or any type of workload with optimal performance and infrastructure cost. OptScale allows ML teams to multiply the number of ML/AI experiments running in parallel while efficiently managing and minimizing costs associated with cloud and infrastructure resources. OptScale MLOps capabilities include ML model leaderboards, performance bottleneck identification and optimization, bulk run of ML/AI experiments, experiment tracking, and more. ... -
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Get Physics Done (GPD)
The first open-source agentic AI physicist
Get Physics Done (GPD) is an open-source project designed to accelerate scientific research in physics by leveraging modern computational tools and automation techniques. It aims to simplify the process of performing simulations, calculations, and experimental analysis by providing structured workflows that integrate computational physics methods with reproducible research practices. The project focuses on reducing the friction involved in setting up experiments, running simulations, and... -
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OpenHarness
Open Agent Harness with a built-in personal agent, Ohmo
OpenHarness is an open-source framework developed to support large-scale machine learning workflows, particularly in the context of training, evaluating, and benchmarking AI models. It provides a structured environment for orchestrating experiments, managing datasets, and standardizing evaluation processes across different models. The project focuses on reproducibility and scalability, allowing researchers and engineers to run consistent experiments while tracking results effectively. It often includes modular components that can be adapted to different machine learning pipelines, enabling flexibility across use cases such as recommendation systems, natural language processing, or multimodal tasks. ... -
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autoresearch
AI agents autonomously run and improve ML experiments overnight
autoresearch is an experimental framework that enables AI agents to autonomously conduct machine learning research by iteratively modifying and training models. Created by Andrej Karpathy, the project allows an agent to edit the model training code, run short experiments, evaluate results, and repeat the process without human intervention. Each experiment runs for a fixed five-minute training window, enabling rapid iteration and consistent comparison across architectural or hyperparameter changes. ... -
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MCiSEE
All of Minecraft, EASILY get Minecraft resources
MCiSEE is an open-source project designed to integrate Minecraft with computer vision and artificial intelligence experiments. The system focuses on capturing visual information from the game environment and exposing it to external programs for analysis or machine learning research. By converting gameplay data into visual or structured formats, MCiSEE enables researchers and developers to build AI agents capable of interacting with the Minecraft environment. -
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Open Vibe
Open Vibe turns Claude Code into a SaaS-building assistant
...After creating a new Wasp app, the user opens an AI coding agent inside the project and lets it fetch course module instructions. The agent then works as both tutor and pair programmer, explaining the system while helping the user build features from plain-language requests. Progress is tracked through JSON files written into the project, making the learning path structured while still letting the user build their own app idea. -
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AutoResearchClaw
Autonomous research from idea to paper. Chat an Idea. Get a Paper 🦞
AutoResearchClaw is an open-source framework designed to automatically generate full academic research papers from a single idea or topic. Built in Python, it orchestrates a multi-stage research pipeline that gathers literature, formulates hypotheses, runs experiments, analyzes results, and writes the final paper. The system retrieves real academic references from sources such as arXiv and Semantic Scholar to ensure credible citations. It can automatically generate code for experiments, run... -
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GenMedia Creative Studio
AI generative media user experience highlighting use of APIs
...The repository also includes experiments and MCP-related materials for connecting generative media capabilities to agent workflows. It is not an officially supported Google product, so it is best treated as a reference implementation and creative sandbox rather than a managed commercial service.
