cours
Introduction to Embeddings with the OpenAI API
Intermédiaire
Updated 12/2024Commencer le cours gratuitement
Inclus gratuitementPremium or Teams
OpenAIIntelligence artificielle3 heures11 vidéos37 exercices3,000 XP6,203Déclaration de réalisation
Créez votre compte gratuit
ou
En continuant, vous acceptez nos Conditions d'utilisation, notre Politique de confidentialité et le fait que vos données sont stockées aux États-Unis.Formation de 2 personnes ou plus ?
Essayer DataCamp for BusinessApprécié par les apprenants de milliers d’entreprises
Description du cours
Enable Powerful AI Applications
Embeddings allow us to represent text numerically, capturing the context and intent behind the text. You'll learn about how these abilities can enable semantic search engines, that can search based on meaning, more relevant recommendation engines, and perform classification tasks like sentiment analysis.Create Embeddings Using the OpenAI API
The OpenAI API not only has endpoints for accessing its GPT and Whisper models, but also for models for creating embeddings from text inputs. You'll create embeddings using OpenAI's state-of-the-art embeddings models to capture the semantic meaning of text.Build Semantic Search and Recommendation Engines
Traditional search engines relied on keyword matching to return the most relevant results to users, but more modern techniques use embeddings, as they can capture the semantic meaning of the text. You'll learn to create a semantic search engine for a online retail platform using OpenAI's embeddings model, so users can more easily find the most relevant products. You'll also learn how to create a product recommendation system, which are built on the same principles as semantic search.Utilize Vector Databases
AI applications in production that rely on embeddings often use a vector database to store and query the embedded text in a more efficient and reproducible way. In this course, you’ll learn to use ChromaDB, an open-source, self-managed vector database solution, to create and store embeddings on your local system.Conditions préalables
Working with the OpenAI APIPython Toolbox1
What are Embeddings?
2
Embeddings for AI Applications
3
Vector Databases
Introduction to Embeddings with the OpenAI API
Cours terminé
Earn Déclaration de réalisation
Ajoutez ces informations d’identification à votre profil LinkedIn, à votre CV ou à votre CVPartagez-le sur les réseaux sociaux et dans votre évaluation de performance
Inclus avecPremium or Teams
S'inscrire maintenantInscrivez-vous 15 millions d’apprenants et commencer Introduction to Embeddings with the OpenAI API Aujourd’hui!
Créez votre compte gratuit
ou
En continuant, vous acceptez nos Conditions d'utilisation, notre Politique de confidentialité et le fait que vos données sont stockées aux États-Unis.