What you'll learn

Skills you'll gain

There are 3 modules in this course

Learners will explore key components of Generative AI, data requirements, and cleaning techniques for LLMs. The course covers model training, optimization, and evaluation methods, including Few-shot, Zero-shot, and Instruction Tuning. Additionally, the course dives into loss functions, alignment techniques, and evaluation metrics such as Perplexity. It also emphasizes the use of GPUs for training, fine-tuning methods like prompt tuning, and Parameter Efficient Fine Tuning (PEFT). Learners will gain expertise in LLM deployment strategies and monitoring with ONNX. This course is divided into three modules, each containing lessons and video lectures. Learners will engage with 4:30-5:00 hours of video content, covering both theoretical concepts and hands-on practices. Each module is equipped with quizzes to reinforce learning and assess understanding. Module 1: Fundamentals of Large Language Models Module 2: Training, Optimization, and Evaluation of LLMs Module 3: LLM Deployment Strategies and Monitoring By the end of this course, a learner will be able to: - Understand the foundational concepts of LLMs, including NLP and training data. - Explore model optimization techniques like loss functions, alignment, and PEFT. - Implement deployment strategies for LLMs and monitor performance using ONNX. This course is intended for professionals looking to deepen their expertise in deploying and optimizing LLMs for Generative AI applications.

Training, Optimization, and Evaluation of LLMs

LLM Deployment Strategies and Monitoring

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