Dive deep into the Transformer Architecture! Trace the evolution from RNNs to Transformers by building attention and full Transformer models from scratch, then leverage Hugging Face to fine-tune and deploy state-of-the-art NLP—gaining both core understanding and real-world skills.
You'll explore why RNNs and LSTMs struggle with long sequences, then build attention mechanisms from the ground up, mastering the QKV paradigm and creating reusable attention modules in PyTorch.
Bringing Transformers to Life: Training & Inference
4 lessons
Course 4
Harnessing Transformers with Hugging Face
4 lessons
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22 practices
You'll systematically build the Transformer architecture from scratch, creating Multi-Head Attention, feed-forward networks, positional encodings, and complete encoder/decoder layers as reusable PyTorch modules.
You'll combine all Transformer components into a complete model, prepare synthetic datasets, implement autoregressive training with teacher forcing, and explore different decoding strategies for sequence generation.
You'll explore the powerful Hugging Face ecosystem and master different pre-trained Transformer architectures, understanding the specific characteristics of BERT, GPT-2, and T5 models along with their tokenizers and use cases.
