Ementorhub

What you'll learn

Explain the concept of attention mechanisms in transformers, including their role in capturing contextual information.

Describe language modeling with the decoder-based GPT and encoder-based BERT.

Implement positional encoding, masking, attention mechanism, document classification, and create LLMs like GPT and BERT.

Use transformer-based models and PyTorch functions for text classification, language translation, and modeling.

Skills you'll gain

Deep Learning

Text Mining

PyTorch (Machine Learning Library)

Applied Machine Learning

Generative AI

Natural Language Processing

Large Language Modeling

There are 2 modules in this course

In this course, you will learn to apply transformer-based models for text classification, focusing on the encoder component. You’ll learn about positional encoding, word embedding, and attention mechanisms in language transformers and their role in capturing contextual information and dependencies. Additionally, you will be introduced to multi-head attention and gain insights on decoder-based language modeling with generative pre-trained transformers (GPT) for language translation, training the models, and implementing them in PyTorch. Further, you’ll explore encoder-based models with bidirectional encoder representations from transformers (BERT) and train using masked language modeling (MLM) and next sentence prediction (NSP). Finally, you will apply transformers for translation by gaining insight into the transformer architecture and performing its PyTorch implementation. The course offers practical exposure with hands-on activities that enables you to apply your knowledge in real-world scenarios. This course is part of a specialized program tailored for individuals interested in Generative AI engineering. This course requires a working knowledge of Python, PyTorch, and machine learning.

Advanced Concepts of Transformer Architecture