Master Techniques to Customize, Optimize, and Deploy Generative AI Models for Real-World Applications
Fine-tuning and Adapting GenAI Models in English
What you’ll learn
- Learn to use Google Colab for unleashing the power of Python’s text analysis and deep learning ecosystem.
- Introduction to the theory and implementation of LLMs and Generative AI.
- Get acquainted with common Large Language Model (LLM) frameworks including LangChain.
- Introduction to the theory and implementation of LLM Optimization.
- Introduction to optimization techniques such as soft prompting.
- Introduction to RAGs.
Course Content
- Introduction –> 9 lectures • 42min.
- Introduction To Generative AI –> 12 lectures • 57min.
- Start With LLMs –> 7 lectures • 28min.
- Fine Tuning LLMs –> 11 lectures • 43min.
- RAGs As a Fine Tuning Mechanism –> 5 lectures • 15min.
Requirements
Fine-tuning and Adapting GenAI Models in English
Unlock the potential of Generative AI with our in-depth course, “Fine-tuning and Adapting GenAI Models in English”. Designed for AI professionals, data scientists, and developers, this course provides a comprehensive exploration of Generative AI concepts, Large Language Models (LLMs), and the practical skills needed to adapt these cutting-edge technologies for real-world applications. Whether you’re new to GenAI or looking to refine your expertise, this course equips you to customize and optimize models to meet diverse use cases effectively.
Course Overview:
This course offers an immersive dive into the principles and applications of Generative AI, with a focus on fine-tuning and adapting LLMs using leading frameworks like OpenAI, Hugging Face, and LangChain. You’ll explore the theoretical foundations of GenAI, learn advanced prompt engineering techniques, and discover how Retrieval-Augmented Generation (RAG) enhances AI capabilities. With hands-on assignments and expert guidance, you’ll master the tools and methodologies required to build powerful AI solutions, such as domain-specific chatbots, summarization systems, and question-answering applications.
Key Learning Outcomes:
- Foundations of Generative AI and LLMs:
Build a strong understanding of Generative AI and the architecture of LLMs, laying the groundwork for advanced fine-tuning techniques. - Frameworks for LLMs:
Gain practical experience with tools like OpenAI APIs, Hugging Face Transformers, and LangChain to customize and deploy LLMs effectively. - Prompt Engineering for Customization:
Learn how to design and optimize prompts to guide LLMs toward delivering precise, contextually relevant outputs. - Fine-tuning Principles and Applications:
Discover how to fine-tune pre-trained models to adapt them for specific domains, improving performance and accuracy. - Retrieval-Augmented Generation (RAG):
Master the integration of external knowledge sources with LLMs to build robust and context-aware AI systems. - Building Real-world Applications:
Apply your knowledge to create solutions for text summarization, question answering, and other real-world use cases using LLMs.
Why Enroll?
Led by an expert in Generative AI with a proven track record of delivering impactful courses, this program combines cutting-edge theory with hands-on practice. By the end of the course, you’ll have the confidence and skills to fine-tune and adapt LLMs for a wide range of applications, from creating conversational agents to deploying intelligent content generation systems.
Ready to Transform Your AI Skills?
Join us and master the art of fine-tuning and adapting Generative AI models. Enroll today to stay ahead in the rapidly evolving field of AI and unlock new opportunities to innovate and lead in the AI-driven future!