index.md

UE 1

Histoire et évolution des modèles de langage

https://www.loom.com/share/06b2f8b9dc5d4e9ab5c8f811e463480c

Apprendre le vocabulaire et les notions du ML/IA

https://www.loom.com/share/bad8f385b6e14634a20c6ae89d5a9606

Comprendre le principe de attention dans les transformeurs

https://www.loom.com/share/e3518b01b5da4521bb0fc72b5a6f56fa https://www.loom.com/share/6a347c3c541e4513ac47211efffcaf90

Les diverse architectures: encodeur, decodeur en enc-dec; les divers approches: seq2seq, casual et mask

https://www.loom.com/share/279b6dd5a81b46a5990750e78ea123f3

Support:

• https://github.com/P2Enjoy/academy-tutorial-python/blob/main/rag/theorie/UE1-introduction.md
• https://github.com/P2Enjoy/academy-tutorial-python/blob/main/rag/theorie/UE1-attention_is_all_you_need-script.md
• https://arxiv.org/abs/2005.11401

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UE 2

Programmer un LLM, apprendre les bases du prompt-engineering

https://www.loom.com/share/10e635fc9e4148ccb0018566c6c94880

Supports:

• https://github.com/P2Enjoy/academy-tutorial-python/blob/main/rag/prompting/02-best_practices.md

Programmer un LLM, les méthodes avancées de prompts et les techniques de fiabilisation ou réflexion

https://www.loom.com/share/9a1c25a3b2b948e78ebf1c82780b1935

Support:

• https://github.com/P2Enjoy/academy-tutorial-python/blob/main/rag/prompting/01-prompt_engineering
• https://github.com/P2Enjoy/academy-tutorial-python/blob/main/rag/prompting/03-general-usage-tips.md

Les techniques d'attaque et les techniques de protection de nos systèmes augmentés à l'IA générative

https://www.loom.com/share/bffc3e253cd24b888fcbdc0360486383

Support:

• https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/
• https://github.com/microsoft/Phi-3CookBook/blob/main/md/02.QuickStart/Huggingface_QuickStart.md
• https://www.kaggle.com/code/ncho11/introduction-to-prompt-engineering-using-mistral
• https://docs.mistral.ai/capabilities/guardrailing/

Les concepts de base: mémoire paramètrisques et non paramètrique, in-context-learning et bases du RAG

https://www.loom.com/share/205800f129c44f1b93a82f82ba8350d5

Support:

• https://github.com/P2Enjoy/academy-tutorial-python/edit/main/rag/rag/inroduction.md
• https://arxiv.org/abs/2005.11401

Créer et alimenter une mémoire non-paramétrique (vectorstore)

https://www.loom.com/share/e0dae18903bf42baaf4d0cedec0e64f6

Deployer en ligne notre assistant juridique

https://www.loom.com/share/458c0f640dc04edb8b647180606c942e

Supports:

• https://github.com/P2Enjoy/academy-tutorial-python/blob/main/rag/prompting/ressource-system-prompt-legifrance.md
• https://www.vie-publique.fr/flux-rss
• https://www.economie.gouv.fr/rss
• https://www.service-public.fr/P10008
• https://platform.openai.com/docs/assistants/tools/file-search?context=streaming&lang=python
• https://platform.openai.com/docs/assistants/tools/function-calling
• https://platform.openai.com/docs/guides/embeddings

Pour aller plus loin:

• https://github.com/openai/openai-cookbook/tree/main/examples

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UE3

Preparation d'un environnment de développement souvrain (offline): reccomandations

https://www.loom.com/share/b7646ecdca694f42a64fbdfeab3d4e51

Présentation des logiciels, matériels et boîtes à outils du développeur LLLM IA en 2024

https://www.loom.com/share/0004a13391ff4f99b969bdf0b6478915

Installation et configuration de l'IDE scientifique IA/ML (jupyter)

https://www.loom.com/share/fa82dc068a7f4dccbb9e948b4b4f489d

Support:

• https://docs.docker.com/engine/install/ubuntu/
• https://ubuntu.com/nvidia
• https://ipywidgets.readthedocs.io/en/stable/user_install.html
• https://github.com/P2Enjoy/academy-tutorial-python/blob/main/rag/rag/01-develoment.md
• https://github.com/P2Enjoy/academy-tutorial-python/blob/main/rag/rag/02-lang-family.md

Installation d'un gestionnaire des modèles de language (Ollama)

https://www.loom.com/share/72a2bfa65c674ec082756aa37d180606

Support:

• https://ollama.com/
• https://ollama.com/search

Introduction à LangGraph, LangChain et mon premier chatbot

https://www.loom.com/share/89c2350fca8c44988bb48be482e8453e

Explication complète de l'architecture de Langchain avec exemples

https://www.loom.com/share/d3a38f9516f94df9b3f764886c1912fe

Support:

• https://github.com/P2Enjoy/academy-tutorial-python/blob/main/rag/rag/04-explain-langchain.md
• https://langchain-ai.github.io/langgraph/concepts/low_level/#graphs
• https://langchain-ai.github.io/langgraph/reference/graphs/
• https://smith.langchain.com/hub
• https://python.langchain.com/docs/concepts
• https://github.com/P2Enjoy/academy-tutorial-python/blob/main/rag/rag/BBS_p1.ipynb

Rebuild de l'environnement d'apprentissage

https://www.loom.com/share/cfc862f52a6c478191ed333ed51ba4ed

Support:

• https://github.com/P2Enjoy/academy-tutorial-python/blob/main/rag/rag/SetupBBS.ipynb

Explication complète de l'architecture de Langgraph avec exemples

https://www.loom.com/share/0b3cfca9031e4674aa4625c75893f926

Support:

• https://github.com/P2Enjoy/academy-tutorial-python/blob/main/rag/rag/Machine_Learning_in_a_RAG_pipeline.ipynb
• https://langchain-ai.github.io/langgraph/concepts/low_level/
• https://langchain-ai.github.io/langgraph/reference/graphs/
• https://langchain-ai.github.io/langgraph/how-tos/configuration/#define-graph
• https://colab.research.google.com/github/langchain-ai/langchain-academy/blob/main/module-1/chain.ipynb
• https://colab.research.google.com/github/langchain-ai/langchain-academy/blob/main/module-1/simple-graph.ipynb
• https://colab.research.google.com/github/langchain-ai/langchain-academy/blob/main/module-1/router.ipynb
• https://colab.research.google.com/github/langchain-ai/langchain-academy/blob/main/module-1/agent.ipynb
• https://colab.research.google.com/github/langchain-ai/langchain-academy/blob/main/module-1/agent-memory.ipynb
• https://langchain-ai.github.io/langgraph/
• https://github.com/langchain-ai/langchain/tree/master/docs/docs/how_to
• https://faiss.ai/
• https://docs.trychroma.com/docs/overview/introduction

LangSmith, créer un environnement d'évaluation pour améliorer vos assistants IA

https://www.loom.com/share/3753ae9cbf4a410184ead8088ca5bf85

Approfondissement de la théorie RAG et présentation de la partie pratique et mise en situation

https://www.loom.com/share/fab22ec9c958433b86c3cc2ce82b0a69

Exemples pratiques et mises en situation (5 niveaux de RAG fonctionnels)

https://www.loom.com/share/543390bc4a2845ddbe2d53fa2f629053

Support:

• https://langchain-ai.github.io/langgraph/how-tos/configuration/
• https://langchain-ai.github.io/langgraph/how-tos/configuration/#configure-the-graph