Standard 9 stops to get here

Positional Encoding

Adding position information to token embeddings so the model understands word order in sequences.

Your route here

9 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. Feature ✓ understood

    A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.

  3. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  4. Neural Network ✓ understood

    A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

  5. Deep Learning ✓ understood

    A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.

  6. Representation Learning ✓ understood

    Learning useful features or representations of data automatically, rather than hand-crafting them.

  7. Embedding ✓ understood

    A list of numbers (a vector) that represents a word, sentence, image or other item, learned so that similar items end up close together.

  8. Attention Mechanism ✓ understood

    A technique that lets a neural network weigh every part of its input when producing each output, focusing on the parts most relevant at that step.

  9. Self-Attention ✓ understood

    A mechanism where each token attends to all other tokens in the sequence to understand contextual relationships.

  10. Positional Encoding · you are here ✓ understood

Where it sits

Before this

Embedding Self-Attention
Positional Encoding

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Language & LLMs Transformer A neural network architecture, introduced in 2017, built from stacked self-attention and feed-forward layers; the basis of nearly every modern large language model. Language & LLMs Context Window The maximum number of tokens an LLM can process at once, including both input prompt and generated output. Also called context length. Language & LLMs Token The basic unit of text that a language model processes, typically representing a word, subword, or character. Tokens are the fundamental building blocks for LLM input and output. Language & LLMs Attention Mechanism A technique that lets a neural network weigh every part of its input when producing each output, focusing on the parts most relevant at that step.