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Fine-tune YOLOv5 on a custom clothing dataset, evaluate its object-detection performance, and run inference on new images.
Explore DeepSeek Coder's capabilities in coding tasks, from simple functions to complex problems and app development. Compare its performance to other AI models in various programming scenarios.
Master Retrieval-Augmented Generation by building a complete system from scratch using Python, LangChain, and Ollama, then deploy it as a production-ready API with Docker.
Discover how to build CogVault, a completely local AI agent that chats with your PDFs and documents using Qwen3, MCP tools, RAG, and Streamlit without cloud dependency.
Explore AutoGen for creating AI agents with LLMs. Learn to build a stock price analyzer and a multi-agent system for cryptocurrency analysis using Python and GPT models.
Discover how to build a local RAG system without vector databases using tree-based indexing, Ollama, and LangChain for financial document analysis.
Evaluate a LayoutLMv3 document classifier, publish it to Hugging Face Hub, run single-image inference, and analyze errors with a confusion matrix.
Explore Llama 3.2 (3B) on Ollama for summarization, data extraction, and labeling. Learn setup, coding, and practical applications in this hands-on tutorial.
Learn to build a private chatbot for analyzing mobile app reviews using LangChain, Ollama, and Qwen 2.5, with step-by-step guidance on local LLM implementation and data analysis.
Explore Microsoft's Phi-4 language model through hands-on testing of summarization, data labeling, and text extraction capabilities using Ollama, with practical demonstrations and performance comparisons.
Master fine-tuning techniques for Llama 3.2 using torchtune, focusing on sentiment analysis for mental health data with practical implementation steps on a single GPU.
Enhance RAG systems with contextual retrieval to improve context chunk quality and generate more accurate answers. Learn implementation techniques and practical applications.
Learn to build a local AI wellness coach with Gemma 3, Ollama, and LangChain that remembers user information, features persistent memory, and includes tool integration—all without cloud dependencies.
Explore Gemma 3's capabilities through practical tests with Ollama, including coding, data extraction, summarization, and RAG implementations in a local environment.
Learn how to fine-tune Qwen3 (0.6B) on custom data for sentiment analysis of cryptocurrency tweets, from data preparation to model evaluation, using LoRA for efficient training on a single GPU.
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