DeepLearning.AI
This course provides an in-depth understanding of text embeddings and their applications in various NLP tasks. Learn to use Google Cloud’s Vertex AI to build a question-answering system and gain proficiency in generating and integrating embeddings into common LLM applications.
In this course, you will explore the properties of word and sentence embeddings and learn how to use them to measure semantic similarity between texts. You will apply text embeddings for tasks such as classification, clustering, and outlier detection. Additionally, you will learn to modify the text generation behavior of an LLM by adjusting parameters like temperature, top-k, and top-p. The course also covers the application of the open-source ScaNN library for efficient semantic search and building a Q&A system by combining semantic search with an LLM.
Data Scientists
Professionals looking to enhance their skills in natural language processing and text embeddings.
Machine Learning Engineers
Engineers who want to apply text embeddings to various NLP tasks such as classification and clustering.
AI Enthusiasts
Individuals with basic Python knowledge interested in learning about text embeddings and their applications.
Gain a comprehensive understanding of text embeddings and their applications in NLP tasks. Learn from industry experts and apply your knowledge to build advanced systems using Google Cloud’s Vertex AI. Ideal for beginners and professionals looking to enhance their skills.
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Basic Python knowledge
Familiarity with natural language processing concepts
Nikita Namjoshi
Product Manager | Google.org Fellow, Google
Nikita Namjoshi is a Product Manager and Google.org Fellow at Woodwell Climate Research Center. She focuses on mapping arctic permafrost thaw for climate action.
Andrew Ng
Founder, DeepLearning.AI
Andrew Ng is the Founder of DeepLearning.AI and Managing General Partner of AI Fund. He is also the Founder and CEO of Landing AI.
Cost
Free
Duration
Dates
Location