Generative AI and LLMs: Architecture and Data Preparation
Completed by Kaleem Ullah Qasim
December 3, 2024
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: Large Language Modeling
- Category: Generative Model Architectures
- Category: Data Pipelines
- Category: Generative AI
- Category: Recurrent Neural Networks (RNNs)
- Category: Natural Language Processing
- Category: PyTorch (Machine Learning Library)
- Category: Data Preprocessing
- Category: LLM Application
- Category: Generative Adversarial Networks (GANs)
- Category: Hugging Face
