Generative AI Fundamentals Course Overview

Overview of the Introduction to Generative AI CourseWith the comprehensive Introduction to Generative AI course from Duke Training Centre, you can unleash the potential of generative AI. Take a deep dive into Python programming over the course of two days (16 hours) and investigate both text- and image-based Large Language Models (LLMs). Learn how to use quantization to optimize models, apply role-based prompting, and use LangChain to create useful applications like chatbots. The course includes long, practical labs that use the Duke Training Centre Data Center and open-source platforms. Gain hands-on experience in object recognition, image captioning, translation, and other related fields to advance your AI abilities. Come along to change the way you think about and use AI in practical applications.

Course Prerequisites

• The following are the minimal requirements for the Introduction to Generative AI course:
• Rudimentary knowledge of Python programming
• knowledge of fundamental programming ideas like variables, loops, and functions
• Interest in the concepts of AI and machine learning

Target Audience for Introduction to Generative AI

For IT professionals looking to advance their AI skills, Duke Training Centre Solutions’ Introduction to Generative AI course provides practical experience with Python, large language models, and RAG systems.

• Scientists of Data
• Engineers in Machine Learning
• Researchers in AI
• Developers of Software
• Consultants in Technology
• AI Fans
• Analysts of Data
• Scientists conducting research
• Entrepreneurs in Technology
• Graduate AI and machine learning students
• IT managers in charge of AI initiatives
• Officers of Innovation
• Architects of Solutions
• AI-interested backend developers
• Python Developers
• Chief Technology Officers and Chief Information Officers, or CIOs
• Academicians teaching AI at universities

Learning Objectives – What you will Learn in this Introduction to Generative AI?

A thorough understanding of Python programming and generative AI is provided by the “Introduction to Generative AI” course, which focuses on developing large language models (LLMs) for text and image tasks, optimizing methods, and creating LLM applications using sophisticated frameworks.

Learning Objectives and Outcomes

Refresh Python Programming Skills:

• Understand and apply basic Python programming concepts.
• Hands-on experience in creating simple generative AI applications using Python and the Hugging Face transformer library.

Text-Based Large Language Models (LLMs):

• Comprehend the architecture and types of LLMs.
• Apply LLMs for tasks like translation, summarization, and sentence similarity.
• Introduction to Ollama and its role in consuming text AI LLMs.
• Perform role-based prompting and consume various LLMs using Ollama.

Image-Based Large Language Models:

• Understand different Image AI models and services.
• Perform tasks such as object detection, image segmentation, image retrieval, image captioning, visual question answering, and zero-shot image classification.

Fine-Tuning LLMs:

• Introduction to the concept and techniques of quantization.
• Optimize model