What are embedding techniques in AI?

What are embedding techniques in AI?

Embedding techniques are methods used to convert categorical data into numerical vectors, making it easier for AI models to process.

Can you explain how word embeddings work?

Word embeddings represent words in a continuous vector space, capturing semantic relationships. For example, 'king' - 'man' + 'woman' results in a vector close to 'queen'.

What are some popular embedding techniques?

Some popular techniques include Word2Vec, GloVe, and FastText, each with unique approaches to generating word vectors.

How do embeddings improve machine learning models?

Embeddings help models understand context and relationships in data, leading to better performance in tasks like classification and clustering.

Are there any limitations to using embeddings?

Yes, embeddings can sometimes capture biases present in the training data and may not generalize well to unseen data.