Python and PyTorch: Hands-On AI for Language Models (S-IT0068)

How long does the training course last?

16,00 hours(s)

2 days

In which language(s) is the training course taught?

When will the next session take place?

Who is organising this training course?

Le Luxembourg Lifelong Learning Centre (LLLC), le centre de formation continue de la Chambre des salariés, dispose d'une large gamme de formations continues qui s’adressent à tout un chacun. Son offre de formation a pour objet de doter les apprenants pour autant que possible du savoir-faire approprié pour maîtriser un environnement de travail, des processus et des technologies, voire des aptitudes sociales, en constante évolution, et ce pour sécuriser au maximum leurs parcours professionnels. Le LLLC propose une panoplie importante de formations: des cours du soir; des séminaires, qui peuvent être adaptés sur mesure selon les besoins des entreprises; des formations universitaires; des formations spécialisées; des formations pour seniors; des certifications professionnelles.

Who is the training course aimed at?

This seminar is designed for intermediate Python programmers and AI practitioners who are comfortable with Python, have basic machine learning knowledge, and are eager to deepen their skills in NLP and PyTorch-based model development.

What are the prerequisites?

Basic programming experience with Python and confidence in writing Python code are required.

What are the aims or the skills being targeted?

Understand the Basics of Language Models
Build and Fine-Tune Custom Models
Apply Techniques to Solve Real-World NLP Problems

What does the training course cover?

Introduction to Language Models and Transformers
- Fundamentals of NLP and language models (LMs), covering key concepts like tokenization, embeddings, and attention mechanisms.
- Overview of Transformer architectures and popular models like BERT and GPT.
Building a Language Model from Scratch with Python and PyTorch
- Step-by-step guide to implementing a simple language model in PyTorch, covering layers, tokenization, and loss functions.
- Training and evaluating the model on a small dataset to understand performance and limitations.
Fine-Tuning Pre-trained Models with Python and PyTorch
- Introduction to Hugging Face Transformers and PyTorch Lightning.
- Hands-on exercise: fine-tuning a pre-trained model for a specific task, such as sentiment analysis or text classification.