- Large Language Modeling
- Data Preprocessing
- Hugging Face
- PyTorch (Machine Learning Library)
- Generative Adversarial Networks (GANs)
- Natural Language Processing
- Recurrent Neural Networks (RNNs)
- Generative AI
- Data Pipelines
- Artificial Intelligence
- Generative Model Architectures
Generative AI and LLMs: Architecture and Data Preparation
Completed by Devansh Sharma
April 20, 2025
5 hours (approximately)
Devansh Sharma'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
