What are the best encoding techniques for different AI models?

What are the best encoding techniques for different AI models?

The best encoding techniques vary by model type. For example, transformers often use token embeddings, while CNNs might use one-hot encoding.

Can you explain token embeddings in more detail?

Sure! Token embeddings convert words into dense vectors, capturing semantic meanings and relationships, which helps models understand context.

What about recurrent neural networks? What encoding works best for them?

Recurrent neural networks (RNNs) typically use word embeddings like Word2Vec or GloVe, which help in capturing sequential data effectively.

Are there any specific techniques for image data?

Yes! For image data, techniques like pixel normalization and feature extraction using CNNs are commonly used to encode visual information.