Generative AI and LLMs: Architecture and Data Preparation
Completed by Matt Woodhall
April 30, 2026
5 hours (approximately)
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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: Responsible AI
- Category: Generative Adversarial Networks (GANs)
- Category: LLM Application
- Category: Recurrent Neural Networks (RNNs)
- Category: Data Preprocessing
- Category: Model Training
- Category: Generative AI
- Category: Natural Language Processing
- Category: Data Pipelines
- Category: Generative Model Architectures
- Category: Hugging Face
- Category: PyTorch (Machine Learning Library)
