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Meta-Learning — Learning to Learn

Expert TopicsMeta-Learning🟢 Free Lesson

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Meta-Learning — Learning to Learn from Few Examples

Discover meta-learning algorithms that enable models to learn new tasks quickly with minimal data. The key to few-shot learning and rapid adaptation.

  • MAML — Model-Agnostic Meta-Learning for fast adaptation
  • Prototypical Networks — Learning metric spaces for classification
  • Reinforcement Learning — Meta-learning with reward signals

"The most important skill is learning how to learn."

Meta-Learning — Learning to Learn

Meta-learning trains models to learn new tasks quickly from few examples.

Meta-Learning Concept

Standard ML vs Meta-LearningStandard MLLearn ONE task from MANY examplesCat Images10,000 samplesDog Images10,000 samplesBird Images10,000 samplesTraining: 30,000 labeled images→ Trained ClassifierTask-specific, large data requiredCannot adapt to new classes without retrainingMeta-LearningLearn MANY tasks from FEW examples eachTask: Cat vs Dog5 shotsTask: Red vs Blue5 shotsTask: Hot vs Cold5 shotsMeta-training: ~100 tasks × 5 examples each→ Meta-Learner (fast adapter)Task-agnostic, learns to adapt quicklyNew task: 5 examples → fast adaptationvs

The Formal Framework

MAML Algorithm

MAML: Model-Agnostic Meta-LearningOuter Loop: Meta-Updateθ ← θ − β∇θ Σ_i ℒ{T_i}(φ_i)Meta-Initialize θLearned across all tasksTask 1: Cat vs DogSupport Set (5-shot):5 labeled examples per classInner Loop (1-5 steps):φᵢ = θ − α∇_θ ℒ_{T_i}(θ)Adapt to task iQuery Set:Unseen examples for meta-lossTask 2: Hot vs ColdSupport Set (5-shot):5 labeled examples per classInner Loop (1-5 steps):φᵢ = θ − α∇_θ ℒ_{T_i}(θ)Adapt to task iQuery Set:Unseen examples for meta-lossTask 3: Red vs BlueSupport Set (5-shot):5 labeled examples per classInner Loop (1-5 steps):φᵢ = θ − α∇_θ ℒ_{T_i}(θ)Adapt to task iQuery Set:Unseen examples for meta-lossTask K: ...Support Set (5-shot):5 labeled examples per classInner Loop (1-5 steps):φᵢ = θ − α∇_θ ℒ_{T_i}(θ)Adapt to task iQuery Set:Unseen examples for meta-lossMeta-loss = Σ_i ℒ_{T_i}(φ_i) → Update θ via β

MAML Algorithm Details

Prototypical Networks

Prototypical Networks: Metric-Based Meta-LearningSupport SetClass AClass BClass CEncodef_θ(x)Embedding Spacec_Ac_Bc_CQuery xd(x,c_A)Classification Rulep(y=k|x) =exp(−d(x, cₖ))Σ_j exp(−d(x, cⱼ))d = Euclidean distancePrototype = Mean Embeddingcₖ = (1/|Sₖ|) Σ_{x∈Sₖ} f_θ(x)

Few-Shot Learning Scenarios

Key Takeaways

What to Learn Next

-> Self-Supervised Learning — Pre-training Revolution Learn about self-supervised learning — pre-training revolution.

-> Transfer Learning — Pre-trained Models Complete Guide Learn about transfer learning — pre-trained models complete guide.

-> Neural Networks Fundamentals — Perceptrons to Deep Learning Learn about neural networks fundamentals — perceptrons to deep learning.

-> Model Evaluation — Metrics, Cross-Validation and Selection Learn about model evaluation — metrics, cross-validation and selection.

-> AutoML — Automated Machine Learning Learn about automl — automated machine learning.

-> ML System Design — Architecture and Production Patterns Learn about ml system design — architecture and production patterns.

←41 Federated Learning43 Self Supervised Learning→

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