LLM Concepts

From transformer architecture to cutting-edge research — each concept explained with intuition, math, and connections to the bigger picture.

Start Module 01

Curriculum

A structured path through the course content.

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Module 01 20 concepts Start here

Foundational Architecture

Core transformer components — self-attention, multi-head attention, feed-forward networks, residual connections, and architectural variants like MoE and sparse attention.

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Module 02 9 concepts

Input Representation

Tokenization, positional encoding, embeddings, and how text becomes numbers.

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Module 03 17 concepts

Training Fundamentals

Optimization, loss functions, scaling laws, and training data.

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Module 04 7 concepts

Distributed Training

Parallelism strategies and distributed systems for large-scale training.

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Module 05 13 concepts

Alignment & Post-Training

RLHF, DPO, reward modeling, and preference learning.

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Module 06 5 concepts

Parameter-Efficient Fine-Tuning

LoRA, adapters, and methods for efficient model adaptation.

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Module 07 18 concepts

Inference & Deployment

Serving, decoding strategies, caching, and quantization.

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Module 08 12 concepts

Practical Applications

RAG, agents, tool use, and prompt engineering.

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Module 09 21 concepts

Safety & Alignment

Attacks, defenses, alignment failures, and guardrails.

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Module 10 7 concepts

Evaluation

Benchmarks, metrics, and evaluation methodology.

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Module 11 27 concepts

Advanced & Emerging

Cutting-edge research and emerging techniques.