14. Meta-Learning & AutoML
The chapter discusses Meta-Learning and AutoML, focusing on automating machine learning tasks with minimal human intervention. Meta-learning enables models to adapt quickly to new tasks using previous experiences, while AutoML streamlines the entire machine learning pipeline. Key methods such as Model-Agnostic Meta-Learning (MAML) and neural architecture search (NAS) are explored, alongside the challenges and future directions for these technologies.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Meta-learning allows for the rapid adaptation of models to new tasks with minimal data.
- AutoML automates the machine learning process, making it accessible even to non-experts.
- Key methodologies such as MAML and NAS provide efficient frameworks for improving model performance.
Key Concepts
- -- MetaLearning
- A paradigm where algorithms learn from previous learning experiences to adapt quickly to new tasks.
- -- AutoML
- The automation of machine learning processes to simplify model building and deployment.
- -- ModelAgnostic MetaLearning (MAML)
- An optimization-based meta-learning technique designed to work with any machine learning model.
- -- Neural Architecture Search (NAS)
- A method for automating the design of neural networks using algorithms that optimize architecture choices.
Additional Learning Materials
Supplementary resources to enhance your learning experience.