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AI-Tutorials/Implementations Notebooks repository is a comprehensive collection of artificial intelligence tutorials and implementation examples intended for developers, students, and researchers who want to learn by building practical AI projects. The repository contains numerous Jupyter notebooks and code samples that demonstrate modern techniques in machine learning, deep learning, data science, and large language model workflows. It includes implementations for a wide range of AI topics such as computer vision, agent systems, federated learning, distributed systems, adversarial attacks, and generative AI. Many of the tutorials focus on building AI agents, multi-agent systems, and workflows that integrate language models with external tools or APIs. The codebase acts as a hands-on learning resource, allowing users to experiment with new frameworks, architectures, and machine learning workflows through guided examples.
Features
- Extensive collection of AI tutorials implemented as runnable notebooks
- Examples covering machine learning, deep learning, and generative AI workflows
- Code for building AI agents and multi-agent systems
- Projects demonstrating computer vision, LLM evaluation, and data science pipelines
- Hands-on demonstrations of modern AI infrastructure and frameworks
- Practical learning resource for experimenting with real AI development workflows
Project Samples
