llm course

The course material is structured progressively: starting with attention mechanisms and positional encoding, moving through pretraining objectives and scaling laws, then into instruction tuning, RLHF alignment, and deployment considerations for production systems.

The curriculum combines theoretical depth with practical implementation, providing PyTorch code for key components alongside conceptual explanations.

Topics include tokenization strategies (BPE, SentencePiece), positional encoding variants (absolute, relative, RoPE, ALiBi), attention mechanism variants (multi-head, grouped-query, sliding window), efficient fine-tuning methods (LoRA, QLoRA, prefix tuning), quantization for inference efficiency, and evaluation frameworks for measuring LLM capability and safety.

The course is designed to be self-contained, not assuming prior exposure to transformer models.

ML engineers building LLM-powered products who want to understand the systems they're deploying beyond surface-level API usage, researchers transitioning from other ML subfields into NLP and language modeling, and practitioners who completed introductory deep learning courses and are ready to specialize in LLMs use this resource.

The combination of growing commercial demand for LLM expertise and the rapid pace of technical development has made structured, up-to-date educational resources like LLM Course increasingly valuable for maintaining relevance in the field.