Large Language Models: Evolution, Impact, and Hands-On Exercises
This course explores the evolution of language models from traditional statistical methods to modern Large Language Models (LLMs) based on deep learning. The course covers the history of language modeling, highlighting key milestones from n-gram models and statistical language modeling, through Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs), to transformers and the emergence of LLMs. The course also presents how the introduction of attention mechanisms and transformer architectures revolutionized NLP tasks such as machine translation, text summarization, and question answering. LLaMA 3 is introduced, examining its key architectural features, innovations, and why it represents a significant advancement in NLP technology. The course culminates with a hands-on programming exercise, using gradient descent, to provide an intuitive understanding of the mechanisms used in model optimization for training and fine-tuning.
What you will learn:
- The historical arc of language models, emphasizing key limitations overcome at each stage
- Understanding of the mathematical and statistical foundations of LLMs
- Why attention mechanisms and transformer architectures revolutionized NLP
- The process of going from transformers to LLMs, and the steps towards training LLMs
- The role of pre-training and fine-tuning with real-world examples
- Understanding of the model optimization process through gradient descent with hands-on exercise
This course is part of the following course program:
Large Language Models Demystified
Instructors
Sai Chand Boyapati
Mr. Boyapati is an internationally recognized expert in software quality assurance (QA), whose groundbreaking work has had a transformative impact on industries worldwide. His influence and contribution in testing span major developments in software products that have redefined their markets.
Mr. Boyapati holds a critical leadership role as Director of Software Quality Assurance in a globally distinguished organization.
In addition to his technical achievements, Mr. Boyapati has served as a peer reviewer and judge in authoritative capacities. He has evaluated numerous research papers for prestigious conferences and hackathons on AI & LLM’s.
He has written extensively on QA, cybersecurity, and artificial intelligence/LLM’s, with articles published in Media. His book, Focus on QA: Redefining Software Testing in the AI-Driven Era, became a bestseller upon release, providing invaluable insights into applying AI to QA processes
Hamza Mohammed, Course Editor
Hamza Mohammed is a Machine Learning Engineer with Samsung Research America. He is an industry expert in deep learning and reinforcement learning, specializing in large language models. Mr. Hamza has a proven research and industry track record applying, optimizing, and accelerating, deep learning and reinforcement learning in various disciplines, including computer vision, natural language processing (including multi-modal modeling), robotics and automation, software engineering and testing, autonomous navigation and ADAS, digital twin simulation, and biomedical imaging. He has designed and optimized ML models and algorithms for edge-compute deployments and is an authority in securing and optimizing AI application security for on-device and on-premise environments. He is a contributor to several open-source projects, an author and peer-reviewer of multiple publications in top-tier ML venues, and is an inventor on key patents. Mr. Hamza holds a B.S. in Electrical Engineering and Computer Sciences, University of California, Berkeley, USA.
Who Should Attend:
AI Researchers and Academics, Data Scientists, Educators and Instructors, Enthusiasts with a Strong Programming Background , Machine Learning Engineers, Software Developers and Programmers, Students (experience in linear algebra and calculus required), Technical Professionals in industry, Technical Product Manager, Technical Leaders
Prerequisites: Basic programming knowledge in Python; no prior experience with language models is required
Course Level: Intermediate
Publication Year: 2025
ISBN: 978-1-7281-7891-2