Natural Language Processing Fundamentals

The Core Idea

Deep learning relies on representing data across layered feature spaces.

These layers allow the model to learn increasingly complex patterns from raw input.

Attention (Q, K, V) = softmax((QKᵀ) / √dₒ)V

Q (Query): Represents the current word being processed.

K (Key): Represents all other words in the input sequence.

live demo · related simulation● LIVE

4. Named Entity Recognition (NER): Identifying and classifying named e

NER focuses on recognizing entities like people, organizations, locations, and dates within text.

This allows for structured extraction of information from unstructured data.

6. Word Embeddings (Word2Vec, GloVe): Representing words as dense vectors based on their context – capturing semantic relationships between words.

Word embeddings transform words into numerical representations that capture meaning and relationships.

This enables models to understand the nuances of language beyond simple keyword matching.

Frequently asked questions

What is deep learning?

Deep learning is a family of machine learning methods that use multi-layer neural networks.

How does attention work in NLP?

The attention mechanism allows the model to focus on the most relevant parts of an input sequence when processing it, improving accuracy and efficiency.

What is Named Entity Recognition (NER) and why is it useful?

NER identifies and classifies named entities within text, such as people, organizations, and locations. This allows for structured data extraction from unstructured text.

What are word embeddings and how do they help NLP models?

Word embeddings represent words as dense vectors, capturing semantic relationships between them. This enables models to understand the context and meaning of words more effectively.

▶ Try it live

Everything above runs in your browser — open Gradient Descent Visualiser and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Gradient Descent Visualiser simulation