Generative AI and LLMs: Architecture and Data Preparation
Completed by Matthew Wesney
March 23, 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: Model Training
- Category: Data Preprocessing
- Category: Large Language Modeling
- Category: Generative Adversarial Networks (GANs)
- Category: Generative AI
- Category: Hugging Face
- Category: Natural Language Processing
- Category: Generative Model Architectures
- Category: LLM Application
- Category: Data Pipelines
- Category: PyTorch (Machine Learning Library)
- Category: Recurrent Neural Networks (RNNs)
