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Book · Machine Learning with PyTorch and Scikit-Learn
Transformers and Attention in PyTorch
A modern NLP milestone moving from recurrence to self-attention, GPT-2 generation, and DistilBERT fine-tuning.
Self-attentionQ/K/VMulti-head attentionGPT-2 generationDistilBERT classification
What this project is about
This milestone moves from recurrence to self-attention. It studies Query/Key/Value attention, multi-head behavior, GPT-2 generation, and DistilBERT fine-tuning.
Implementation focus
The learning was the modern NLP shift: self-attention scales representation learning beyond RNNs and connects directly to current LLM systems.
Key results and artifacts
- Attention score matrix: 8×8 in the basics notebook
- GPT-2 hidden states: 1×5×768 example
- DistilBERT fine-tuning logs across 3 epochs
What I learned
This topic connects directly to the current goal: mastering Hugging Face, Transformers, and LLM systems.
Milestone deep dive
What this project proves in my learning path.
A modern NLP milestone moving from recurrence to self-attention, GPT-2 generation, and DistilBERT fine-tuning. 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 framingWhat 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.
ImplementationWhat 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.
ConceptsCore concepts
Self-attentionQ/K/VMulti-head attentionGPT-2 generationDistilBERT classification
These tags show the technical ideas this milestone added to my toolkit.
Learning resultWhat changed after it
This topic connects directly to the current goal: mastering Hugging Face, Transformers, and LLM systems.
Important outputs and evidence
- Attention score matrix: 8×8 in the basics notebook
- GPT-2 hidden states: 1×5×768 example
- DistilBERT fine-tuning logs across 3 epochs
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.