This module introduces the foundational components of the Transformer model, including self-attention, multi-head attention, input embeddings, and positional encoding. Learners will explore how these elements interact to process language data and understand the role of normalization in model architecture. Practical exercises guide students through implementing and analyzing key sublayers of the Transformer.

This module delves into how transformer models tackle complex Natural Language Understanding (NLU) tasks, including multi-sentence comprehension and coreference resolution. Learners will examine performance metrics, human baselines, and advanced benchmarks like SuperGLUE to understand the evolving capabilities of AI. Real-world examples such as MultiRC and the Winograd Schema Challenge illustrate the depth and challenges of emergent and downstream tasks.

This module introduces modern machine translation techniques using tools like Google Trax, Google Translate, and Gemini Transformers. Learners will preprocess translation datasets, implement evaluation metrics such as BLEU scores, and explore the practical applications of transformer-based models in multilingual contexts.

This module guides learners through the process of fine-tuning BERT models for natural language processing tasks using Hugging Face. You will explore key concepts such as tokenization, next-sentence prediction, optimizer configuration, and evaluation metrics like MCC. By the end, you'll gain practical skills in adapting pretrained transformer models to specific NLP problems.

This module guides learners through the process of building and pretraining a custom RoBERTa-based transformer model using Hugging Face tools. Participants will gain hands-on experience with tokenizer training, dataset preparation, model parameter exploration, and practical pretraining for NLP applications such as generative AI customer support.