Generative AI Using SAS

Übersicht

Generative Artificial Intelligence (GenAI) is a rapidly developing area of machine learning, with application across business, government, and academia. In this course, you will learn about different types of GenAI and see examples of how SAS can enhance your efforts to make the most of these techniques.

Learn How To:

1. Explain what generative AI is and how it fits into the broader AI landscape. 2.

Describe several types of GenAI systems. 3. Name some of the key challenges and opportunities in making a trustworthy AI system. 4.

Generate synthetic data with Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Networks (GANs). 5. Explain how Large Language Models (LLMs) generate meaningful text. 6.

Classify text for LLMs using Bidirectional Encoder Representations from Transformers (BERT). 7. Improve the accuracy and relevance of LLM output using Retrieval Augmented Generation (RAG).

Who Should Attend:

Learners who want to know more about the techniques that comprise GenAI and how to make use of them with SAS Prerequisites:

Before taking this course, you should have some background in statistics and machine learning using SAS. You can gain this knowledge by taking the following courses:

1.

Statistics You Need to Know for Machine Learning 2. Machine Learning Using SAS Viya

Lehrplan

• Defining Generative AI: Unraveling the Concept
Learn what generative AI is all about and see examples in practice.
• Types of Generative AI Systems
Learn about the input-output framework in generative AI.
• Transformers and Large Language Models (LLMs)
Learn why transformers are such an important aspect of generative AI.
• Synthetic Minority Oversampling Technique (SMOTE) in SAS
Learn how to generate synthetic data with Synthetic Minority Oversampling Technique (SMOTE).
• Generative Adversarial Networks (GANs) in SAS
Learn how to Generate synthetic data with Generative Adversarial Networks (GANs).
• Classifying Text Using BERT
Learn how to classify text for LLMs using Bidirectional Encoder Representations from Transformers (BERT).
• Retrieval Augmented Generation (RAG)
Learn how to improve the accuracy and relevance of LLM output using Retrieval Augmented Generation (RAG).

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Unterrichtet von

Catherine Truxillo

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Fachgebiete

Computer Science