Prerequisites: Requires deep expertise in machine learning, deep learning architectures (especially Transformers), optimization techniques, and prior experience implementing and training large models. Familiarity with foundational meta-learning concepts is beneficial.

Level: Expert

What You'll Learn

• Advanced Meta-Learning Algorithms

  Implement and analyze sophisticated gradient-based, metric-based, and optimization-based meta-learning algorithms.

• Foundation Model Adaptation

  Design and apply effective few-shot adaptation strategies specifically for large foundation models.

• Scalability Techniques

  Address computational and memory challenges when applying meta-learning to large-scale models.

• Theoretical Understanding

  Grasp the theoretical guarantees and limitations of different meta-learning approaches in the context of foundation models.

• Parameter-Efficient Methods

  Analyze and compare meta-learning with parameter-efficient fine-tuning (PEFT) techniques for few-shot adaptation.

• Implementation Expertise

  Gain practical experience implementing meta-learning adaptation pipelines for foundation models.