Deep Learning | Video 5 | Part 2 | Word-to-Vector: Contextual Word | Venkat Reddy AI Classes

Course Materials https://github.com/venkatareddykonasani/Youtube_videos_Material To keep up with the latest updates, join our WhatsApp community: https://chat.whatsapp.com/GidY7xFaFtkJg5OqN2X52k In this video, we delve into the concept of Word-to-Vec and how it creates numerical representations for words based on their context. The key idea is not just to convert words randomly into numbers, but to capture their meaning by considering the surrounding context. Word-to-Vec, introduced by J.R. Firth in 1957, emphasizes understanding words based on the company they keep—their context. By using a context window size (e.g., three words), we can determine the context in which a word appears. For instance, if we take "King" with a context size of three, the context might be "King is a strong man." To create a Word-to-Vec model: Create Training Samples: Pair each word with its surrounding context words. Build a Neural Network Model: Use these pairs to train a shallow neural network. Generate Word Vectors: The model's hidden layer output provides numerical representations (vectors) for each word based on its context. The neural network ensures that words with similar contexts get similar numerical representations, while those with different contexts get different representations. This representation captures relationships between words. For example, "King" and "man" might have similar vectors due to their contextual connection. You can visualize these word vectors in three dimensions. Words with similar meanings or contexts cluster together, making it easier to see relationships between them. By performing vector arithmetic (e.g., King - man + woman), you can even generate new word vectors and predict where they might fall in relation to others. Word-to-Vec is a powerful technique used in natural language processing for tasks like sentiment analysis, document classification, and recommendation systems. It's an efficient way to convert words into meaningful numerical representations that capture semantic relationships. Explore this video to understand how Word-to-Vec works and how it can enhance various NLP applications. #WordToVec #NaturalLanguageProcessing #NLP #MachineLearning #DataScience #ai #promptengineering #genai #deeplearning