🧠 Embedding Models: From Architecture to Implementation

Welcome to the "Embedding Models: From Architecture to Implementation" course! 🧑‍🏫 The course delves deep into the architecture and capabilities of embedding models, widely used in AI applications to capture the meaning of words and sentences.

📘 Course Summary

In this course, you’ll explore the evolution of embedding models, from word to sentence embeddings, and build and train a simple dual encoder model. 🧠 The hands-on approach will enable you to grasp the technical concepts behind embedding models and how to effectively use them.

Detailed Learning Outcomes:

1. 🧩 Embedding Models: Learn about word embedding, sentence embedding, and cross-encoder models, and how they are utilized in Retrieval-Augmented Generation (RAG) systems.

2. 🧠 Transformer Models: Understand how transformer models, specifically BERT (Bi-directional Encoder Representations from Transformers), are trained and used in semantic search systems.

3. 🏗️ Dual Encoder Architecture: Gain knowledge of the evolution of sentence embedding and understand the formation of the dual encoder architecture.

4. 🔧 Training with Contrastive Loss: Use contrastive loss to train a dual encoder model, with one encoder trained for questions and another for responses.

5. 🔍 RAG Pipeline: Utilize separate encoders for questions and answers in a RAG pipeline and observe the differences in retrieval effectiveness compared to a single encoder model.

🔑 Key Points

• 🏛️ In-depth Understanding: Gain a deep understanding of embedding model architecture and learn how to train and use them effectively in AI applications.
• 🧩 Embedding Models in Practice: Learn how to apply different embedding models such as Word2Vec and BERT in various semantic search systems.
• 🏋️ Dual Encoder Training: Build and train dual encoder models using contrastive loss to enhance the accuracy of question-answer retrieval applications.

👩‍🏫 About the Instructor

• 👨‍🏫 Ofer Mendelevitch: Head of Developer Relations at Vectara, Ofer brings extensive experience in embedding models and their implementation in real-world AI applications.

🔗 To enroll in the course or for further information, visit 📚 deeplearning.ai.