Embeddings and Sequence Models in Deep Learning
Practical notes on embeddings, learned representations, and sequence models.
This article is part of the Deep Learning hub.
Embeddings and sequence models help deep learning systems work with structured information such as text, speech, time series, and ordered events.
Embeddings
An embedding maps an item into a dense vector.
Examples include:
- Words
- Sentences
- Images
- Products
- Documents
The goal is to place related items near each other in a vector space.
Why embeddings matter
Embeddings are useful because they allow models to learn relationships between items.
Instead of treating every item as unrelated, the model can learn patterns such as similarity, context, and association.
Sequence models
Sequence models process ordered inputs.
Examples include:
- Text
- Audio
- Logs
- Time series
The order matters. A sequence model must account for both the items and their position in the sequence.
Encoder-decoder pattern
The encoder-decoder pattern is common for sequence tasks.
The encoder reads the input and produces a representation. The decoder uses that representation to generate an output.
This pattern appears in translation, summarisation, captioning, and other structured prediction tasks.
Attention
Attention helps a model focus on relevant parts of the input.
This is useful when different output steps depend on different parts of the input sequence.
Attention later became a foundation for many modern language systems.
Practical checks
When working with embeddings and sequences, ask:
- What does each vector represent?
- How is sequence length controlled?
- How is padding handled?
- Are rare items handled properly?
- Is the evaluation split appropriate?
- Can examples near an embedding be inspected?
Engineering takeaway
Embeddings and sequence models are powerful because they learn structure from data.
They should be evaluated carefully, especially when sequence length, data drift, or rare examples can affect behaviour.
Last reviewed: June 2026. Validate modelling choices and framework behaviour against current documentation before production use.