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Understanding LLMs: A Complete Foundation
An L100 foundations course. Before calling an API or launching an agent, understand what is under the hood. This conceptual, visual course demystifies large language models: how a Transformer works, how it is trained, what hardware it runs on, and how much it costs. No code required — you leave with a solid understanding to make informed AI decisions.
Duration
2 days
Level
Beginner
Price
9.99 EUR/month (all courses included)
Max group
15 participants
What you will learn
+Explain how a neural network and deep learning work
+Describe the Transformer architecture and the attention mechanism
+Understand the training pipeline: pre-training, RLHF, DPO
+Tell model families apart and know when to use which
+Assess hardware constraints: GPU, VRAM, quantization
+Analyze LLM economics: training, inference, and per-token costs
Course program
Module 1: Neural networks and deep learning
3h30- From the biological neuron to the artificial neuron
- What a deep neural network is
- Supervised, unsupervised, and reinforcement learning
- Interactive demo: watch a network learn
Module 2: The Transformer architecture
3h30- Tokenization and embeddings: how text becomes numbers
- The attention mechanism explained visually
- Encoder vs decoder: BERT, GPT, and beyond
- Scaling: why larger models work better
Module 3: Training and alignment
3h30- Pre-training: massive data, objectives, and costs
- RLHF and DPO: making a model helpful and safe
- Model families: GPT, Claude, Llama, Mistral — positioning
- Open-source vs proprietary: selection criteria
Module 4: Hardware, economics, and ethics
3h30- GPU vs CPU, VRAM, INT4/INT8 quantization
- Demo: run a model locally with Ollama
- LLM economics: training, inference, and per-token pricing
- Ethics: hallucinations, bias, environmental impact
Ready to get started?
9.99 EUR/month — All courses included, cancel anytime