Large Language Models (LLMs) Concepts

Description

In this course, you will:

• Explore how LLMs, through their various applications, are accelerating the development of human-like artificial intelligence and altering industry. 
• Investigate the difficulties and complexities related with language modeling.
• Learn about the obstacles of training LLMs and how fine-tuning can help you overcome them. 
• Learn how N-shot learning approaches allow for rapid adaption of pre-trained models in the face of little labeled data.
• Discover the core building blocks of LLM training, such as pre-training strategies. 
• Learn to comprehend complicated concepts like transformer architecture and the attention process intuitively. explains an advanced fine-tuning technique and summarizes the training procedure for an LLM.
• Explore the essential factors to consider when training LLMs, including as big data availability, data quality, accurate labeling, and the consequences of biased data. 
• Explore various LLM hazards such as data privacy, ethical considerations, and environmental effect. 

Syllabus:

• Introduction to Large Language Models (LLM)
• Building Blocks of LLMs
• Training Methodology and Techniques
• Concerns and Considerations