- Prompt Engineering
- Deep Learning
- PyTorch (Machine Learning Library)
- Data Pipelines
- Natural Language Processing
- Generative AI
- Data Processing
- Text Mining
- Large Language Modeling
- Artificial Intelligence
Generative AI and LLMs: Architecture and Data Preparation
Completed by Roberto Marroquin
July 6, 2024
5 hours (approximately)
Roberto Marroquin's account is verified. Coursera certifies their successful completion of Generative AI and LLMs: Architecture and Data Preparation
What you will learn
Differentiate between generative AI architectures and models, such as RNNs, transformers, VAEs, GANs, and diffusion models
Describe how LLMs, such as GPT, BERT, BART, and T5, are applied in natural language processing tasks
Implement tokenization to preprocess raw text using NLP libraries like NLTK, spaCy, BertTokenizer, and XLNetTokenizer
Create an NLP data loader in PyTorch that handles tokenization, numericalization, and padding for text datasets
Skills you will gain
