Transformers & Attention
Understand self-attention and transformer data flow by implementing the important pieces and applying them to intent routing.
Production project
Support Ticket Intent Router
A support platform needs to route incoming text to payments, delivery, account, and technical queues with a transformer-based classifier.
Who this is for
- •ML engineers who use transformers but want to understand the internals
- •Developers preparing for deeper LLM work
How this helps
- •You stop treating transformer diagrams as black boxes
- •You gain the foundation needed for serious LLM engineering discussions
What you will learn
The capabilities, not just the keywords.
- •Explain queries, keys, values, and scaled dot-product attention
- •Trace shapes through a self-attention block
- •Understand why positional information is required
- •Relate attention to modern transformer encoders
- •Apply transformer representations to classification
Real production project
Support Ticket Intent Router
A support platform needs to route incoming text to payments, delivery, account, and technical queues with a transformer-based classifier.
What we build
- •Tokenisation flow
- •attention inspection
- •classifier head
- •training/evaluation loop
- •routing demo
Stack
- •Python
- •PyTorch
- •Transformers concepts
Production concerns
- •Latency
- •confidence thresholds
- •class imbalance
- •fallback routing
- •model size
Finished deliverables
- •Working classifier demo
- •attention walkthrough notebook
- •architecture notes
- •evaluation checklist
Prerequisites
Required
- •Basic neural-network understanding
- •Comfort with vectors/matrices and Python
Helpful, but optional
- •PyTorch basics
- •embeddings
Before the session
Come ready to build.
- •Python 3.11+
- •PyTorch environment
- •Basic linear algebra refresh
What to expect
What you get
Something useful after the call ends.
- •Code/notebook
- •transformer mental-model notes
- •shape-tracing worksheet
- •next-step LLM roadmap
Where to go next
Deep Learning → Transformers → LLM Engineering
Every session can be booked independently. If you already know the prerequisite material, skip ahead.
Useful before this
No earlier session is mandatory.
Teaching proof
Review the evidence first.
See workshop delivery, technical projects, and teaching proof before booking.
Ready?
Request DL06: Transformers & Attention
Share your current level and what you want to build. The session can be adapted without changing its core outcome.
Request this sessionFAQ
Can I book only this session?
Yes. Every session is designed to work independently.
Can I bring my own project?
Yes, when it fits the session outcome. We can map the concepts onto your project instead of the default example.
What if I already know part of the topic?
We can move faster through familiar material and spend more time on implementation, failure modes, and production trade-offs.
Is the session recorded?
Recording is not promised by default. Confirm this before the session if you need it.
What should I install?
The preparation section above lists the default setup. You will receive any session-specific setup notes before the call.
Are API or cloud charges included?
No. Any third-party API or cloud usage is paid directly through your own account unless explicitly agreed otherwise.