- Natural Language Processing
- Generative AI
- Generative Adversarial Networks (GANs)
- Text Mining
- Artificial Intelligence
- Generative Model Architectures
- Recurrent Neural Networks (RNNs)
- PyTorch (Machine Learning Library)
- Data Preprocessing
- Large Language Modeling
- Hugging Face
- Data Pipelines
Generative AI and LLMs: Architecture and Data Preparation
Completed by Puneet Akhouri
October 22, 2024
5 hours (approximately)
Puneet Akhouri'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
