This segment explains the methodology of generating word embeddings using n-grams. It describes how n-grams (sequences of n words) are extracted from text and used to train a neural network to predict surrounding words. The training process implicitly captures semantic relationships, where semantically similar words appear in similar contexts and receive similar gradient feedback, ultimately leading to a meaningful vector representation for each word. Neural networks excel with image data, but text poses challenges. Directly using ASCII values is uninformative. One-hot encoding, while distinct, is impractically large. Word embeddings solve this: neural networks learn vector representations where semantically similar words have similar vectors. This is achieved by training a network to predict surrounding words (n-grams), then using the hidden layer activations as the word embedding. Pre-trained embeddings are used for efficiency, converting text to vectors for neural network input. This segment explores the concept of one-hot encoding for representing words as input to a neural network. It details the method, highlighting its advantage of ensuring semantically different words have distinct representations. However, it also critically analyzes its significant drawback: the massive dimensionality and impracticality for large vocabularies, leading to the need for a more efficient and semantically meaningful representation.