What you will learn in Generative AI with Large Language Models Course
- Understand the fundamentals of generative AI and the lifecycle of large language models (LLMs), including data gathering, model selection, performance evaluation, and deployment.
- Gain in-depth knowledge of transformer architectures, their training processes, and how fine-tuning enables adaptation to specific use cases.
- Apply empirical scaling laws to optimize model objectives concerning dataset size, computational resources, and inference requirements.
- Implement state-of-the-art training, tuning, inference, and deployment methods to maximize model performance within project constraints.
- Explore real-world applications and challenges of generative AI through insights from industry researchers and practitioners.
Program Overview
Generative AI Use Cases, Project Lifecycle, and Model Pre-training
⏱️ 5 hours
- Introduction to generative AI and LLMs, their use cases, and tasks.
- Understanding the transformer architecture and text generation techniques.
- Exploration of the generative AI project lifecycle and model pre-training processes.
- Hands-on lab: Summarize dialogue using generative AI.
Fine-tuning and Evaluating Large Language Models
⏱️ 4 hours
- Techniques for fine-tuning LLMs with instruction datasets.
- Understanding parameter-efficient fine-tuning (PEFT) and addressing catastrophic forgetting.
- Evaluation methods for LLM performance.
- Hands-on lab: Fine-tune a generative AI model for dialogue summarization.
Reinforcement Learning and LLM-powered Applications
⏱️5 hours
- Introduction to reinforcement learning with human feedback (RLHF) for LLMs.
- Techniques like chain-of-thought prompting to enhance reasoning and planning abilities.
- Addressing challenges such as knowledge cut-offs and implementing information retrieval strategies.
- Hands-on lab: Fine-tune FLAN-T5 with reinforcement learning to generate more positive summaries.
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Job Outlook
- Proficiency in generative AI and LLMs is increasingly sought after in roles such as AI Developer, Machine Learning Engineer, and Data Scientist.
- Understanding transformer architectures and fine-tuning techniques positions learners for opportunities in cutting-edge AI research and application development.
- Skills acquired are applicable across industries leveraging AI for natural language processing, content generation, and automation.
Specification: Generative AI with Large Language Models
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