The foundations of large language models, including their architecture and training framework, as well as their capabilities, applications, and limitations, are covered in this advanced course. Pre-requisites: Basic Machine learning concepts and Python knowledge.

Course Objectives:

• Gain a comprehensive understanding of the architecture, components, and underlying principles of large language models.
• Learn the various techniques and frameworks used to train LLMs, including data collection, preprocessing, and model fine-tuning.
• Analyze the capabilities of LLMs, including natural language processing tasks, and explore their applications across different industries.
• Discuss the limitations, challenges, and ethical considerations associated with deploying LLMs in real-world scenarios.
• Develop practical skills in implementing, fine-tuning, and evaluating LLMs using Python-based tools and libraries.

Course Outcomes:

• Demonstrate a solid understanding of the foundational concepts, architecture, and training methodologies of large language models.
• Apply practical skills to implement and fine-tune LLMs for various natural language processing tasks using Python.
• Critically evaluate the performance of LLMs in different contexts and understand their strengths and limitations.
• Recognize and discuss the ethical implications and challenges associated with the use of LLMs in different applications.
• Design and implement solutions using LLMs to solve complex problems in various domains, demonstrating a thorough understanding of their capabilities and limitations.

Tools and Technologies Used:

• Python: The primary programming language used
• PyTorch and TensorFlow: Deep learning frameworks for building and training LLMs
• Transformers Library (Hugging Face): A popular library for working with pre-trained LLMs
• Google Colab: A cloud-based platform providing free access to GPUs for training and experimenting with LLMs
• Git and GitHub: For version control and collaboration on code development