Training / Models & Machine Learning
Advanced · 2 days
Transformers from First Principles
A two-day course for technically strong practitioners: build a usable understanding of transformer models by implementing simplified components, without unnecessary mathematical formalism.
2 days · Advanced · Models & Machine Learning · Developers, ML engineers and strong practitioners
Overview
Transformer models sit under almost every production language system in current use. Many engineers can call an API without being able to reason about attention, context, or why scaling and quantisation behave as they do. This course closes that gap by building the mechanism from the pieces.
The course starts with vectors, embeddings and matrix operations at an intuitive level, then implements attention, self-attention, queries, keys and values. Positional information, transformer blocks, feed-forward layers and normalisation are assembled into a working picture of a model. Training, loss, token prediction and inference are connected to that implementation.
The mathematics is kept as light as the subject allows. You write code for simplified transformer components and inspect them, rather than being asked to reproduce a paper. Scaling and production implications are drawn out as the implementation grows.
You leave able to read a transformer architecture, discuss it with ML specialists, and make better choices about context, fine-tuning and local inference.
Audience
Developers, ML engineers and technically strong practitioners who want a deeper, implementation-level understanding of transformer models.
Prerequisites
Comfortable writing code and with high-school algebra. Some familiarity with arrays or tensors helps. No requirement for formal machine-learning theory or advanced calculus.
Duration
2 days
Can be reduced to a dense one-day briefing without the full implementation labs, or extended with more training-loop work.
Learning outcomes
- 01
Explain embeddings, attention and transformer blocks in implementation terms.
- 02
Implement simplified self-attention, including queries, keys, values and positional information.
- 03
Assemble a transformer block: attention, feed-forward layers, residual connections and normalisation.
- 04
Connect training, loss and token prediction to the forward pass the class has built.
- 05
Describe inference, scaling and the practical limits that follow from the architecture.
- 06
Use the implementation as a basis for better decisions about context, fine-tuning and runtime.
Outline
- 01
Representations
- Vectors, embeddings and similarity as geometry rather than as mysticism.
- Matrix operations as the work the hardware is doing.
- Tokens in, vectors through, logits out.
- Lab: implement embeddings and a tiny prediction head.
- 02
Attention
- Queries, keys and values; why attention is a routing mechanism.
- Self-attention on a short sequence, inspected step by step.
- Positional information and what breaks if it is omitted.
- Lab: implement scaled dot-product attention and visualise weights.
- 03
The transformer block
- Multi-head attention, feed-forward layers, residual connections and normalisation.
- Stacking blocks; what depth changes and what it costs.
- Lab: assemble a small transformer and run a forward pass.
- 04
Training, inference and scale
- Loss, token prediction and a minimal training step.
- Inference as a loop, and the difference between teacher-forced training and generation.
- Scaling, context length and why implementation details become product constraints.
- Lab: train a tiny model on a toy corpus and generate from it.
Practical work
You implement simplified transformer components in code: embeddings, attention, a block, and a tiny training and generation loop. The objective is understanding, not reproducing a production-scale model.
Takeaways
- Working code for simplified transformer components
- Visualisations of attention and token prediction
- A compact architecture reference for production discussions
- A clearer map from internals to context, fine-tuning and inference choices
Delivery
Cognitecta delivers private corporate training, on-site or as remote live training. Courses can be run as published, or adapted to your organisation’s stack, domain and experience level.
Instructor
Nicholas Johnson, AI architect and software engineer. He has a degree in Artificial Intelligence and around twenty years of professional technology training, including hundreds of courses for engineering teams and large organisations. About.
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