Description
In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications. This course is designed for developers who have a foundational understanding of LLMs and want to deepen their knowledge. By taking this course, you'll learn to deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection to performance evaluation and deployment.
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The course covers the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to various specific use cases. You will also explore empirical scaling laws to optimize the model's objective function across dataset size, compute budget, and inference requirements. The course includes instruction from expert AWS AI practitioners who actively build and deploy AI in business use cases today.
This is an intermediate course, so it is recommended that you have some experience coding in Python and familiarity with the basics of machine learning, such as supervised and unsupervised learning, loss functions, and data splitting. If you have taken the Machine Learning Specialization or Deep Learning Specialization from DeepLearning.AI, you’ll be ready to dive deeper into the fundamentals of generative AI.
The course consists of 17 videos, 7 readings, and 3 assignments, providing a comprehensive learning experience. Upon completion, you will gain foundational knowledge, practical skills, and a functional understanding of how generative AI works, enabling you to make informed decisions for your projects and companies.
- CategoryCorporate Training & E-Learning
Generative AI with Large Language Models's Core Features
Instructor: Chris Fregly
Skill Level: Intermediate
Certificate Offered: Yes
Course Count: 1
Duration Estimate: 3 weeks
Assignments: 3
Videos: 17
Readings: 7
How to use Generative AI with Large Language Models?
Understand: Learn the fundamentals of generative AI and LLMs.
Explore: Dive into the transformer architecture and its training methods.
Apply: Use empirical scaling laws to optimize models.
Deploy: Implement best practices for deploying generative AI in real-world applications.
Generative AI with Large Language Models's Use Cases
- Model Optimization
- Generative AI Deployment
- Training LLMs
- Fine-tuning Models
- Business Applications
