Python + AI: Vector embeddings скачать в хорошем качестве

Python + AI: Vector embeddings

In our second session of the Python + AI series, we'll dive into a different kind of model: the vector embedding model. A vector embedding is a way to encode a text or image as an array of floating point numbers. Vector embeddings make it possible to perform similarity search on many kinds of content. In this session, we'll explore different vector embedding models, like the OpenAI text-embedding-3 series, with both visualizations and Python code. We'll compare distance metrics, use quantization to reduce vector size, and try out multimodal embedding models. If you'd like to follow along with the live examples, make sure you've got a GitHub account. 📌 This session is a part of a series. Learn more here: https://aka.ms/PythonAI/2 Explore the slides and episode resources: https://aka.ms/pythonai/resources Check out the demos: https://aka.ms/python-openai-demos Chapters: 00:08 – Welcome & Housekeeping 01:03 – Introduction to Vector Embeddings 02:24 – Why Vector Embeddings Matter 03:32 – How Embedding Models Work 06:01 – Comparing Embedding Models 10:55 – Generating Embeddings with OpenAI 20:59 – Understanding Similarity Spaces 24:47 – Cosine Similarity Explained 34:02 – Vector Search with Exhaustive Search 40:01 – Approximate Nearest Neighbor (ANN) Search 46:54 – Compressing Embeddings: Quantization 56:17 – Compressing Embeddings: Dimensionality Reduction 59:57 – Oversampling for High-Quality Retrieval 1:00:08 – Wrap-Up & Resources #MicrosoftReactor #learnconnectbuild [eventID:26293]