Exploring Large Language Model’s with Python

Description:

Large Language Models (LLMs) have revolutionized natural language processing (NLP) by demonstrating remarkable capabilities in understanding, generating, and manipulating human language. This 3-hour training session provides an in-depth exploration of LLMs, focusing on their implementation using the Python programming language. Participants will delve into the underlying architecture of LLMs, learn how to work with pre-trained models, and gain hands-on experience in fine-tuning LLMs for specific NLP tasks. Through practical exercises and case studies, attendees will discover the diverse applications of LLMs in text generation, sentiment analysis, and beyond. By the end of the session, participants will be equipped with the knowledge and tools to harness the power of LLMs in their own projects and applications.

Duration:  Half day (3 Hrs)

Course Code: BDT343

Learning Objectives:

• Understand the principles behind Large Language Models
• Gain hands-on experience in working with LLM libraries in Python
• Learn techniques for fine-tuning and deploying pre-trained LLMs
• Explore applications of LLMs in text generation, sentiment analysis, and more

• Basic understanding of Python programming language
• Familiarity with fundamental concepts of natural language processing (NLP)
• Knowledge of machine learning concepts is beneficial but not required

This course is suitable for:

• Python developers interested in NLP and AI
• Data scientists looking to incorporate LLMs into their projects
• Students and professionals seeking to enhance their understanding of advanced NLP techniques

This course is suitable for:

• Python developers interested in NLP and AI
• Data scientists looking to incorporate LLMs into their projects
• Students and professionals seeking to enhance their understanding of advanced NLP techniques

Course Outline:

• Introduction to Large Language Models
• Key concepts - Attention mechanisms and Transformer architectures
• Introduction to popular LLM libraries (e.g., Hugging Face's Transformers)
• Setting up the development environment and installing necessary libraries
• Working with Pre-trained Models
• Loading and using pre-trained LLMs for various tasks (e.g., text generation, classification)
• Fine-tuning pre-trained models on custom datasets for specific tasks
• Hands-on exercises in text generation, sentiment analysis, and other NLP tasks
• Exploration of real-world use cases and examples of LLM applications
• Best practices for training, fine-tuning, and evaluating LLMs

Training material provided: Yes (Digital format)