Deep Learning | Video 5 | Part 3 | Converting Text to Vectors - Word2Vec | 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 Learn how to convert text into numerical vectors using Word2Vec. This technique is crucial for natural language processing (NLP) tasks. Manual Word to Vector Conversion: Typically, in deep learning, we convert text data into vectors using Word2Vec to preserve contextual relationships between words. We manually clean a corpus of text, remove stop words, and then convert words into vectors by preserving their context using a defined window size. Building the Word2Vec Model: Define a context window size (e.g., 2) to establish relationships between nearby words. Prepare input-output pairs where words are inputs and their surrounding context words are outputs. Build a shallow neural network model using Keras to convert words into one-hot encoding. Extract the hidden layer output, which serves as the word vector representation. Automating with Gensim: Gensim offers a simpler approach. Use Gensim's Word2Vec model to automatically convert words to vectors. Provide parameters like size (number of dimensions), window (context window size), and min_count (minimum word occurrence threshold). Leveraging Pre-trained Models: Google's pre-trained Word2Vec model is available for direct use, trained on a massive dataset of news articles. Load the model with a single line of code and use it to find similar words or establish word relationships. Benefits and Applications: Word2Vec allows for semantic analysis, identifying word relationships (e.g., "France" is to "Paris" as "Italy" is to "Rome"). The model can be utilized in various NLP tasks, such as sentiment analysis, machine translation, or information retrieval. Understand the power of Word2Vec in NLP tasks. Learn how to implement it manually, automate with Gensim, or use pre-trained models for efficient text representation. #Word2Vec #NLP #TextAnalytics #MachineLearning #DeepLearning #ai #datascience #dataanalysis #genai #promptengineering