Generative AI and LLMs: Architecture and Data Preparation
Completed by Abel Adamu Buba
June 11, 2024
5 hours (approximately)
Abel Adamu Buba'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
- Category: Generative Model Architectures
- Category: Recurrent Neural Networks (RNNs)
- Category: Generative Adversarial Networks (GANs)
- Category: Data Preprocessing
- Category: PyTorch (Machine Learning Library)
- Category: Large Language Modeling
- Category: Model Training
- Category: Natural Language Processing
- Category: Hugging Face
- Category: Data Pipelines
- Category: LLM Application
- Category: Generative AI
