Generative AI and RAG
Introduction
Dive into the transformative world of Generative AI and RAG (Retrieval-Augmented Generation). This course is designed to equip you with the foundational knowledge and practical skills to understand and apply these cutting-edge technologies. From theoretical concepts to real-world applications, you’ll gain insights into how Generative AI and RAG are shaping industries worldwide.
What You’ll Learn
- The fundamentals of Generative AI and its underlying principles.
- How RAG integrates retrieval systems with generative models for enhanced outputs.
- Techniques for training and fine-tuning generative models.
- Practical applications of RAG in various industries such as healthcare, finance, and customer service.
- Ethical considerations and challenges in deploying Generative AI and RAG systems.
Requirements
- Basic programming knowledge, preferably in Python.
- Familiarity with machine learning concepts and frameworks.
- A laptop or computer capable of running AI development tools.
Detailed Course Description
This course provides a comprehensive exploration of Generative AI and RAG. We begin with an introduction to generative models, including an overview of algorithms such as GANs, VAEs, and Transformer-based architectures. Next, we delve into the concept of RAG, explaining how retrieval mechanisms are coupled with generative models to enhance the accuracy and relevance of generated outputs.
Hands-on projects are a key component of this course, enabling you to apply what you’ve learned. You’ll implement retrieval-augmented generation pipelines using popular frameworks such as Hugging Face and OpenAI. Additionally, the course will cover optimization techniques, scalability, and the latest advancements in generative AI.
By the end of the course, you’ll be able to confidently design and deploy your own Generative AI and RAG systems, paving the way for innovative solutions in your field of expertise.
Who is This Course For?
- Data scientists and AI practitioners looking to expand their skill set.
- Developers interested in creating intelligent, generative systems.
- Business professionals exploring the potential of AI-driven solutions.
- Students and researchers aiming to understand the latest in AI technologies.
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