Ementorhub

Generative AI Language Modeling with Transformers

This course is part of multiple programs. Learn more

Instructors: Joseph Santarcangelo +2 more

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What you'll learn

Explain the role of attention mechanisms in transformer models for capturing contextual relationships in text

Describe the differences in language modeling approaches between decoder-based models like GPT and encoder-based models like BERT

Implement key components of transformer models, including positional encoding, attention mechanisms, and masking, using PyTorch

Apply transformer-based models for real-world NLP tasks, such as text classification and language translation, using PyTorch and Hugging Face tools

Skills you'll gain

PyTorch (Machine Learning Library)

Text Mining

Deep Learning

Applied Machine Learning

Generative AI

Natural Language Processing

Machine Learning Methods

Large Language Modeling

There are 2 modules in this course

The course covers multi-head attention, self-attention, and causal language modeling with GPT for tasks like text generation and translation. You will gain hands-on experience implementing transformer models in PyTorch, including pretraining strategies such as masked language modeling (MLM) and next sentence prediction (NSP). Through guided labs, you’ll apply encoder and decoder models to real-world scenarios. This course is designed for learners interested in generative AI engineering and requires prior knowledge of Python, PyTorch, and machine learning. Enroll now to build your skills in NLP with transformers!

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