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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.

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