
Advanced Large Language Models (LLMs) and Generative AI (Level 2)
This advanced course delves deep into the cutting-edge techniques and applications of Large Language Models (LLMs) and Generative AI. It covers advanced topics such as transformer architectures, cross-modal generative models, reinforcement learning for content generation, and multimodal AI. Learners will explore the challenges of scaling LLMs, fine-tuning models for specific applications, and optimizing generative models for efficiency. The course also addresses ethical concerns, bias detection, and responsible AI development, while providing hands-on projects focused on real-world applications.
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Course Duration:
27 Hours
Level:
Advanced

Course Objectives
Master the theory and architecture of transformer models and advanced LLM techniques.
Implement state-of-the-art generative AI models like GANs, VAEs, and diffusion models.
Understand and apply transfer learning, fine-tuning, and reinforcement learning in generative models.
Build cross-modal systems and explore AI-driven applications in text, image, and audio generation.
Evaluate and optimize LLMs and generative models for real-world deployment.
Explore ethical considerations, fairness, and bias mitigation strategies in advanced AI systems.
Prerequisites
Strong understanding of machine learning and deep learning concepts.
Familiarity with neural networks, backpropagation, and training techniques.
Experience with Python and popular ML frameworks (e.g., TensorFlow, PyTorch).
Prior exposure to Large Language Models (LLMs) or completion of an introductory AI/LLM course is recommended.
