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Deep Learning Specialization · Sequence Models

Transformer Architecture from Scratch

A from-scratch TensorFlow/Keras implementation of the Transformer: positional encoding, masks, scaled dot-product attention, encoder, decoder, and full model.

Positional encodingPadding masksLook-ahead masksScaled dot-product attentionEncoder-decoder Transformer
GitHub

What this project is about

This milestone implements the Transformer building blocks: positional encoding, masks, scaled dot-product attention, multi-head attention layers, encoder, decoder, and final model.

Implementation focus

It became the conceptual bridge to LLMs: modern language models are built on attention, token positions, residual blocks, normalization, and feed-forward sublayers.

Key results and artifacts

  • Implemented EncoderLayer, DecoderLayer, Encoder, Decoder, and Transformer classes
  • Unit tests passed for the full assembly
  • Bridge from RNN sequence models to modern NLP/LLM architecture

What I learned

This milestone became the bridge into the current NLP/LLM path: attention, not recurrence, is the core abstraction of modern language models.

Milestone deep dive

What this project proves in my learning path.

A from-scratch TensorFlow/Keras implementation of the Transformer: positional encoding, masks, scaled dot-product attention, encoder, decoder, and full model. The milestone is included because it shows a complete learning unit: a problem, a data representation, an implementation path, measurable outputs, and a lesson that later connects to larger AI systems.

Input layertokens, masks, and embeddings
Model layerattention-based sequence architecture
Evaluation layertests, metrics, plots, or qualitative output
Artifact layercontext-aware sequence representations or generated tokens
Problem framing

What I was trying to solve

This page is not just a lab description. It explains how I transformed the assignment idea into an engineering story: what the model receives, what it optimizes, what output it should produce, and how the result can be inspected.

Implementation

What I coded or assembled

The implementation focus was the mechanism behind the result: functions, model blocks, training logic, preprocessing, evaluation, and the small technical details that make the notebook work rather than only look correct.

Concepts

Core concepts

Positional encodingPadding masksLook-ahead masksScaled dot-product attentionEncoder-decoder Transformer

These tags show the technical ideas this milestone added to my toolkit.

Learning result

What changed after it

This milestone became the bridge into the current NLP/LLM path: attention, not recurrence, is the core abstraction of modern language models.

Important outputs and evidence

  • Implemented EncoderLayer, DecoderLayer, Encoder, Decoder, and Transformer classes
  • Unit tests passed for the full assembly
  • Bridge from RNN sequence models to modern NLP/LLM architecture

How I would explain it in an interview

I would describe this milestone by starting from the data representation, then explain the model or algorithm, then show what was measured. The important part is not only the final number; it is the ability to reason about why the method works, what assumptions it makes, and what limitations should be improved next.