React Developer Salary: From Entry-Level to Senior Engineer
November 29, 2023
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Recommended experience
Beginner level
Ideal for data scientists, ML engineers, and NLP enthusiasts. Learn to build and implement embedding models for semantic retrieval systems.
Recommended experience
Beginner level
Ideal for data scientists, ML engineers, and NLP enthusiasts. Learn to build and implement embedding models for semantic retrieval systems.
Gain an in-depth understanding of the architecture behind embedding models; and learn how to train and use them.
Learn how to use different embedding models such as Word2Vec and BERT in various semantic search systems.
Learn how to build and train dual encoder models using contrastive loss, enhancing the accuracy of question-answer retrieval applications.
August 2024
Only available on desktop
Join our new short course, Embedding Models: From Architecture to Implementation! Learn from Ofer Mendelevitch, Head of Developer Relations at Vectara.
This course goes into the details of the architecture and capabilities of embedding models, which are used in many AI applications to capture the meaning of words and sentences. You will learn about the evolution of embedding models, from word to sentence embeddings, and build and train a simple dual encoder model. This hands-on approach will help you understand the technical concepts behind embedding models and how to use them effectively. In detail, you’ll: 1. Learn about word embedding, sentence embedding, and cross-encoder models; and how they can be used in RAG. 2. Understand how transformer models, specifically BERT (Bi-directional Encoder Representations from Transformers), are trained and used in semantic search systems. 3. Gain knowledge of the evolution of sentence embedding and understand how the dual encoder architecture was formed. 4. Use a contrastive loss to train a dual encoder model, with one encoder trained for questions and another for the responses. 5. Utilize separate encoders for question and answer in a RAG pipeline and see how it affects the retrieval compared to using a single encoder model. By the end of this course, you will understand word, sentence, and cross-encoder embedding models, and how transformer-based models like BERT are trained and used in semantic search. You will also learn how to train dual encoder models with contrastive loss and evaluate their impact on retrieval in a RAG pipeline.
DeepLearning.AI is an education technology company that develops a global community of AI talent. DeepLearning.AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future.
Hands-on, project-based learning
Practice new skills by completing job-related tasks with step-by-step instructions.
No downloads or installation required
Access the tools and resources you need in a cloud environment.
Available only on desktop
This project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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Google Cloud
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Google Cloud
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