Machine Learning Crash Course: Embeddings

Neuron networks represent words as embeddings, which are high-dimensional vectors. A common method to generate these embeddings is through a neural network with an input layer using one-hot encoding, followed by an embedding layer, and an output layer. The model is trained using large text corpora to predict the context of words, creating embedding vectors that encode word meanings. Similar words end up closer together in vector space. This process involves significant training time and memory usage.