Introduction

AI is constantly evolving and it is essential to stay abreast of the latest developments. Professor of Artificial Intelligence Andrew Ng and founder of DeepLearning.AI, has launched a new short course titled “Embedding Models: Described as “From Architecture to Implementation”, this course aims to delve into the origins of Model Embedding, its architecture, and the operationalization of models that are fundamental to today’s AI systems. Regardless of your level of expertise in using AI, this course will help you gain the understanding and practical knowledge of embedding models and their application.

Learning outcomes

• Learn about word embeddings, sentence embeddings, and cross-encoder models and their application in Retrieval-Augmented Generation (RAG) systems.
• Gain insights as you train and use transformer-based models like BERT in semantic search systems.
• Learn how to build dual encoder models with contrast loss by training separate encoders for questions and answers.
• Build and train a dual encoder model and analyze its impact on retrieval performance in a RAG pipeline.

Course overview

The course provides an in-depth exploration of different embedding models. It starts with historical approaches and covers the latest models in modern AI systems. Speech interfaces, a key component of AI systems, rely on embedding models. These models help machines understand and respond accurately to human language.

This course covers fundamental theories and relies on the students’ understanding. It guides them through the process of building and training a dual encoder model. At the end, participants will be able to apply these models to practical problems, particularly in semantic search systems.

Detailed course content

Now let’s dig deeper into the content of the course.

Introduction to Embedding Models

This section begins with an analysis of the evolution of embedding models in artificial intelligence. You will discover how the first AI systems attempted to solve the problem of how to represent text data and the evolution towards embedding models. The important tools needed to understand how embedding models work are reviewed in the course, starting with the concepts of vector space and similarity.

You will learn about the use of embedding models in current artificial intelligence, such as in recommender systems, natural language processing and semantic search. This will form the basis needed for further analysis in the following sections.

Word embeddings

This module provides an overview of what word embeddings are; these are methods used in transforming words into continuous vectors that are in a multidimensional space. You will be informed about how these embeddings model the semantic context between words from their application to large text collections.

It is important to explain that the course will describe the most popular models for learning word embeddings, namely Word2Vec, GloVe, FastText. At the end of this example, you will understand the nature of these algorithms. And also how they create the vectors for words.

In this section, word embeddings in real word applications are discussed for realizing the information processing tasks mentioned below, such as machine translation, opinion mining, information retrieval, etc. To show how word embeddings work in practice, real-world examples and scenarios are included.

From Embeddings to BERT

By extending previous approaches to word embedding, this section articulates developments that have contributed to models such as BERT. This is because you will learn how previous models have drawbacks and how BERT deals with these using the context of each word in a sentence.

The course also describes how BERT and similar models come up with contextualized word embeddings – a word can mean something different under different words. This kind of approach aims to eradicate high-level language comprehension and has improved many NLP tasks.

You will explore the architecture of BERT, including the use of transformers and attention mechanisms. The course provides insights into how BERT processes text data, how it is trained on large amounts of text, and its impact on the field of NLP.

Dual Encoder Architecture

This module introduces the concept of dual encoder models. These models use different embedding models for different input types, such as questions and answers. You will learn why this architecture is effective for applications such as semantic search and question answering systems.

This course also describes how the dual encoder models work and the structure that these models will have, to distinguish them from the single encoder models. Here you will find information about what a dual encoder entails, how each of the encoders is trained to come up with an embedding that is relevant to its input.

This section discusses the benefits of using dual encoder models, such as improved search relevance and better matching between queries and results. Real-world examples show how dual encoders are applied in various industries, from e-commerce to customer support.

Practical implementation

In this practical exercise, we will go through the process of constructing the dual encoder model from scratch. There is TensorFlow or PyTorch where you will learn how to configure this architecture, feed your data and train the model.

You will learn how to train your dual encoder model in the course, specifically using contrastive loss, which is of utmost importance in training the model to learn how to distinguish relevant and irrelevant data pairs. Also how to further optimize the model to perform better on certain tasks.

You will learn how to evaluate the efficiency of the model you have built and trained. The course discusses various measures to assess the quality of embeddings, including accuracy, recall, and F1-score. In addition, you will learn how to compare the performance of a dual encoder model with a single encoder model.

Last but not least, the course will briefly explain how to deploy your trained model into production. The course will teach you how to fine-tune the model and get it to perform optimally, especially when ingesting new data.

Who should participate?

This course is intended for a wide range of learners, including:

• Data Scientists: They want to deepen their understanding of embedded models and their applications in AI.
• Machine Learning Engineers: Interested in building and implementing advanced NLP models in production environments.
• NLP enthusiasts: Discover the latest developments in embedding models and apply them to improve semantic search and other NLP tasks.
• AI practitioners: With basic knowledge of Python, who would like to expand their skills by learning how to implement and refine embedding models.

Whether you are familiar with generative AI applications or just starting your journey in NLP, this course will provide valuable insights and practical experience that will help you advance in the field.

Register now

Don’t miss the chance to expand your knowledge of model embedding. Register for free today and start building the future of AI!

Conclusion

If you’re looking for a detailed overview of embeddings and how they work, Andrew Ng’s new course on embedding models is the way to go. By the end of this course, you’ll be well-positioned to solve tough AI problems related to semantic search and any other problem involving embeddings. Whether you’re looking to expand your expertise in AI or learn the latest strategies, this course is a boon.

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