Generative AI and LLMs: Architecture and Data Preparation
Completed by Vinay Thamatam
March 13, 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: Generative AI
- Category: Data Pipelines
- Category: Model Training
- Category: Hugging Face
- Category: LLM Application
- Category: PyTorch (Machine Learning Library)
- Category: Generative Model Architectures
- Category: Data Preprocessing
- Category: Large Language Modeling
- Category: Recurrent Neural Networks (RNNs)
- Category: Natural Language Processing
- Category: Generative Adversarial Networks (GANs)
