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Tracks and articles

01What is LLM — A Probabilistic Machine That Pieces Together Erased Experimental Notes 02What is a Neural Network — A Pattern-Recognizing Machine Made of Dials and Switches 03How Neural Networks Learn — Gradient Descent Rolling Down the Free Energy Landscape 04How Does Backpropagation Work — Tracing the Reverse Causality of a Signal Cascade 05Transformers and Embeddings — Mapping Words to Cell Type Space 06Attention Mechanism — Contextual Judgement of the Tumor Microenvironment 07Prompt Engineering — Designing Conditions for Probabilistic Machines 08RAG and Context Expansion — Augmenting LLMs with a Lab's Knowledge Base 09Agent and Tool Use — A Loop for Automatically Stitching Together Experiments 10Context Window Management — Handling Limited Working Space Wisely 11Hallucination and Alignment — Why Probabilistic Machines Invent Plausible Lies 12PyTorch Basics — The First Step to Implementing Principles with Code 13HuggingFace and Commercial APIs — Mapping the Model Ecosystem 14Claude Code and Cursor — Developing with an AI Coding Agent 15Bio and AI Integration — Applying Lessons from Parts 1-14 to a Lab Workflow

Biostatistics with Python33

Fullstack Development55

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