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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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Step-by-step guide to mastering digital project management skills through Learni's bootcamp launching in April 2026, including enrollment tips, curriculum details, and career prospects.
Don't let this gap widen
Sans maîtrise des Transformers, vos modèles IA stagnent à des performances inférieures de 40% aux standards du marché, rendant les applications en NLP et vision par ordinateur obsolètes.
Les entreprises perdent en moyenne 250 000 € par an en projets ratés ou retardés, avec 70% des incidents liés à des architectures mal optimisées et des mises en pratique défaillantes.
Chaque mois sans ces compétences essentielles, vos équipes gaspillent 200 heures en debugging inutile, exposant l'entreprise à une perte de parts de marché face à des concurrents agiles.
Votre carrière et celle de votre équipe risquent l'obsolescence rapide dans un secteur où les Transformers dominent 90% des avancées IA.
The Maîtriser les Transformers : Architecture, Applications et Mise en Pratique training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Maîtriser les Transformers : Architecture, Applications et Mise en Pratique training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Maîtriser les Transformers : Architecture, Applications et Mise en Pratique training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
• État de l’art en NLP – De RNN/LSTM aux Transformers • Comprendre Self-Attention et l’importance des embeddings • Présentation détaillée du modèle Transformer et son architecture (encoder/decoder) • Démonstrations interactives sur colab
• Focus sur BERT et GPT : différences, usages courants • Utilisation de modèles pré-entraînés (HuggingFace, TensorFlow, PyTorch) • Mise en place de pipelines pour classification, génération de texte, résumé automatique • Exercices pratiques : implémenter une tâche d’analyse de sentiment
• Fine-tuning sur un jeu de données propre – techniques avancées • Gestion de la mémoire et optimisation des temps d’entraînement • Interprétabilité et limites éthiques des modèles Transformers • Déployer un modèle NLP Transformer en production (API Flask/FastAPI) • Atelier final : projet complet de fine-tuning et mise en production
Target audience
Développeurs, data scientists, chercheurs et ingénieurs souhaitant comprendre et utiliser les modèles Transformers en intelligence artificielle
Prerequisites
Bases en Python et compréhension fondamentale du machine learning
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