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In this hands-on course, you will learn to build and utilize a fully local, privacy-preserving AI research assistant powered by LLMs such as those hosted on Ollama or LM Studio. Named the Local Deep Researcher, this system enables autonomous, iterative research on any given topic by cycling through intelligent querying, information retrieval, summarization, and reflection—all without needing cloud APIs or external services.You’ll explore the full pipeline, including:Generating intelligent search queries using an LLMGathering and parsing web search documentsCreating and updating concise summaries of findingsReflecting on current summaries to identify knowledge gapsRefining search queries to address those gapsProducing a clean, markdown-based final research report with source attributionKey Learning Outcomes:Deploy and configure LLMs locally using Ollama or LM StudioUnderstand autonomous agentic loops for research tasksDesign and implement research workflows for summarization and knowledge refinementAutomate the generation of structured, referenced reportsWho This Course Is For:Researchers and students looking for AI-powered research toolsDevelopers building intelligent personal knowledge agentsPrivacy-conscious professionals needing offline research supportEnthusiasts interested in agentic AI, LLM automation, and local-first toolsTools & Tech Stack:PythonDeepSeek / Other local LLMsOllama or LM StudioLangChain (optional)Markdown for report generationBy the end of the course, you'll have your own fully functioning Local Deep Researcher capable of producing structured reports with iterative reasoning—all on your local machine.

This course provides an in-depth, hands-on journey through the complete implementation of a GPT-style language model, similar to OpenAI’s GPT-2. Built entirely using PyTorch, this codebase shows you how to tokenize data, construct Transformer-based models (including causal self-attention and MLP blocks), train efficiently with distributed training (DDP + gradient accumulation), evaluate with loss and accuracy metrics (including HellaSwag tasks), and generate text in an autoregressive fashion.You will not just use Hugging Face tools—you will replicate how GPT works at the core. This means building positional embeddings, attention heads, model layers, training loops, learning rate schedulers, validation steps, and generation logic—all from scratch.Whether you're an AI researcher, developer, or enthusiast, this course will give you an insider's view of what powers ChatGPT and how you can create your own scaled-down version for specific domains or experiments.