[CS316] Create a Semantic Search Engine and Vector Database, with Python, NumPy, and Word Embeddings

Title: Create a Semantic Search Engine with Python, NumPy, and Word Embeddings

Description:
In this video, we dive into building a semantic search engine using Python, NumPy, and pre-trained word embeddings like GloVe. Learn how to transform questions and sentences into vectors, calculate cosine similarity using the dot product, and leverage these techniques to find the most relevant answers from a database of questions. This step-by-step guide covers:

🔹 Setting up and installing necessary libraries such as Gensim for word embeddings.
🔹 Loading pre-trained GloVe embeddings to capture word semantics.
🔹 Converting sentences into vectors using averaging of word embeddings.
🔹 Understanding and implementing cosine similarity with NumPy to compare query vectors with stored question vectors.
🔹 Building a vector-based database and implementing a basic semantic search functionality to find answers based on user queries.
🔹 Hands-on coding demonstration for students or developers looking to master vector representations and semantic search concepts.

By the end of this video, you will have a working semantic search engine that can find relevant answers based on user input, all while understanding the fundamentals of word embeddings, vector operations, and cosine similarity!

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[akeelomairi]

Dear Dr. Anis, I sent you an email in which I asked you some help about your code of securing Mavlink protocol. Thanks