Generative AI and LLMs: Architecture and Data Preparation

Overview

What you’ll learn

1. - Differentiate between generative AI architectures and models, such as RNNs, Transformers, VAEs, GANs, and Diffusion Models.
2. - Describe how LLMs, such as GPT, BERT, BART, and T5, are used in language processing.
3. - Implement tokenization to preprocess raw textual data using NLP libraries such as NLTK, spaCy, BertTokenizer, and XLNetTokenizer.
4. - Create an NLP data loader using PyTorch to perform tokenization, numericalization, and padding of text data.