Quick Start Guide to Large Language Models (LLMs)

via Coursera Specialization

Overview

This specialization is a quick start guide to help people use and launch LLMs like GPT, Llama, T5, and BERT at scale. It presents a step-by-step approach to building and deploying LLMs, with real-world case studies to illustrate the concepts, and covers topics such as constructing agents, fine-tuning a Llama 3 model with RLHF, building recommendation engines with Siamese BERT architectures, launching an information retrieval system with OpenAI embeddings and GPT-4, and building an image captioning system with the vision transformer and GPT. This guide provides clear instructions and best practices for using LLMs and will be a valuable resource for anyone looking to use LLMs in their projects.

Syllabus

Course 1: Quick Start Guide to Large Language Models (LLMs): Unit 1
Course 2: Quick Start Guide to Large Language Models (LLMs): Unit 2
Course 3: Quick Start Guide to Large Language Models (LLMs): Unit 3

Courses

0 reviews
6 hours 18 minutes
View details

This course is designed to guide you through the evolution of natural language processing (NLP), from its historical roots to the cutting-edge advancements of today. You'll delve into the mechanics of modern deep learning architectures, exploring ground breaking concepts like attention and alignment mechanisms. Gain hands-on experience with leading LLMs such as ChatGPT, Llama, and T5, and discover how these models are revolutionizing AI-driven solutions. Through practical lessons, you'll learn about semantic search and build your own retrieval-augmented generation systems. Additionally, you'll gain experience in prompt engineering, enabling you to communicate effectively with LLMs. By the end of this course, you'll be equipped with the skills to effectively leverage the power of LLMs.
0 reviews
5 hours 24 minutes
View details

This course explores optimization, fine-tuning, and AI alignment. You'll gain hands-on experience with OpenAI's fine-tuning APIs, learning to customize models for specific needs across various domains, from research to business applications. Discover advanced prompt engineering techniques to refine and enhance model outputs, ensuring they align with human expectations and preferences. Through detailed case studies, you'll learn to create powerful recommendation engines using customized embeddings, outperforming standard solutions. Additionally, the course addresses the financial aspects of AI, demonstrating how to achieve superior performance without excessive costs.
0 reviews
8 hours 7 minutes
View details

This course explores building novel architectures tailored to unique challenges. You'll gain hands-on experience in building custom multimodal models that integrate visual and textual data, and learn to implement reinforcement learning for dynamic response refinement. Through practical case studies, you'll learn advanced fine-tuning techniques, such as mixed precision training and gradient accumulation, optimizing open-source models like BERT and GPT-2. Transitioning from theory to practice, the course also covers the complexities of deploying LLMs to the cloud, utilizing techniques like quantization and knowledge distillation for efficient, cost-effective models. By the end of this course, you'll be equipped with the skills to evaluate LLM tasks and deploy high-performing models.