Word Embedding Videos

Word Embedding Tweets

Count Vectorizer & HashingVectorizer 

There are different types of embeddings

• token embeddings (for characters or subwords)
• word embeddings (for individual words)
• sentence embeddings (for entire sentences)
• document embeddings (for longer texts)
• Image Embedding

Embeddings are learned using

1️⃣ Unsupervised methods like Word2Vec and GloVe rely on statistical properties of raw text.

2️⃣ Self-supervised methods like BERT's MLM create tasks that teach models to predict missing words or sentence relationships.

Words are great, but if we want to use them as input to a neural network, we have to convert them to numbers. One of the most popular methods for assigning numbers to words is to use a Neural Network to create Word Embeddings. One of the most popular Word Embedding tools → word2vec