- Large Language Modeling
- Prompt Engineering
- Natural Language Processing
- Generative AI
- Transfer Learning
- PyTorch (Machine Learning Library)
- Performance Tuning
Generative AI Engineering and Fine-Tuning Transformers
Completed by Swaminathan Arumugam
November 3, 2024
8 hours (approximately)
Swaminathan Arumugam's account is verified. Coursera certifies their successful completion of Generative AI Engineering and Fine-Tuning Transformers
What you will learn
Sought-after, job-ready skills businesses need for working with transformer-based LLMs in generative AI engineering
How to perform parameter-efficient fine-tuning (PEFT) using methods like LoRA and QLoRA to optimize model training
How to use pretrained transformer models for language tasks and fine-tune them for specific downstream applications
How to load models, run inference, and train models using the Hugging Face and PyTorch frameworks
Skills you will gain
