Master Transformers from Scratch: BERT, ViT & AI Search

About this course

Course description

Transformers power almost everything in modern machine learning: text classification, search engines, named entity recognition, and even image models. But when you first open a transformer diagram, it looks very complicated.

Attention. Queries, keys, and values. Multiple heads. Positional encoding. Residual connections. Layer normalization. And many matrices.

This course makes all of these ideas simple. You will not just learn how to use a transformer. You will understand what is happening inside it.

How we learn

We start from the very beginning and build up one step at a time:

• The idea: why we need attention, explained with simple examples.
• The math: vectors, dot products, and matrices, with real numbers you can follow by hand.
• How the pieces connect: self-attention, queries, keys and values, multi-head attention, the encoder block, and positional encoding.
• The code: we build important parts in PyTorch.
• Real applications: we fine-tune real pre-trained models with Hugging Face.
• What we build
• Fine-tune BERT for text classification (disaster tweets)Use BERT for Named Entity Recognition

Knowledge distillation with DistilBERTVision Transformers for image classification (cassava leaf disease)A semantic search engine with a two-tower architecture

Important ideas come back

Transformers are many small ideas that are connected to each other, so this course repeats important concepts on purpose. You will see an idea again, sometimes with a different example and sometimes at a deeper level.

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