Introduction to embeddings with Microsoft Foundry and C# | Capture the MEANING of data скачать в хорошем качестве

Introduction to embeddings with Microsoft Foundry and C# | Capture the MEANING of data

Introduction to embeddings that explains how they capture meaning in high-dimensional space and semantic search with Microsoft Foundry and C#. 🔍 In this deep dive, you’ll learn step by step how to move from simple text to high-dimensional vector representations that actually capture meaning using Microsoft Foundry’s embedding models and secure RBAC access. What you’ll discover: 🔥 📌 Converting arrays of strings intro primitive Binary vectors 📌 Soft Label encoding for richer, more nuanced interpretation 📌 Various similarity metrics including Cosine Similarity, Euclidian and Manhattan Distances, Dot Product and Hamming Distance 📌 Embedding spaces and various data modalities 📌 Using Foundry’s embedding models with EmbeddingClient + TensorPrimitives for simple vector similarity search 📌 Secure RBAC setup for accessing Microsoft Foundry resource using DefaultAzureCredential class 📝 Blog post: https://deployedinazure.com/introduct... ------ 0:00 Intro 0:48 Arrays of strings 4:32 Binary vectors 7:35 Soft Label encoding 13:46 Similarity metrics 16:52 Real-world vectors 19:58 Embedding spaces 20:48 Single vs Multi data modality 21:52 Embedding models in Microsoft Foundry 26:20 Secure access to Microsoft Foundry using RBAC 31:22 Generating embeddings using Microsoft Foundry 37:06 Outro ------ #azure #foundry #vectorsearch #csharp #rbac Follow me 🔔 🌐 Blog: https://deployedinazure.com 💻 GitHub: https://github.com/deployed-in-azure 🔗 LinkedIn:   / deployed-in-azure