Month 12: Advanced Deep Learning Techniques

Month 12: Advanced Deep Learning Techniques

Week 1: Transfer Learning and Fine-Tuning

• Day 1: Introduction to Transfer Learning
• Day 2: Understanding Pretrained Models
• Day 3: Fine-Tuning Pretrained Models
• Day 4: Transfer Learning Across Domains and Tasks
• Day 5: Practical application and a mini-project

Week 2: Unsupervised and Self-Supervised Learning

• Day 1: Introduction to Unsupervised Learning: Clustering, Dimensionality Reduction
• Day 2: Introduction to Self-Supervised Learning
• Day 3: Contrastive Learning and Predictive Coding
• Day 4: Self-Supervised Learning for Vision and Language
• Day 5: Practical application and a mini-project

Week 3: Meta-Learning and Few-Shot Learning

• Day 1: Introduction to Meta-Learning
• Day 2: Understanding Few-Shot Learning
• Day 3: Model-Agnostic Meta-Learning (MAML)
• Day 4: Prototypical Networks and Relation Networks
• Day 5: Practical application and a mini-project

Week 4: Interpretability and Explainability

• Day 1: Introduction to Interpretability and Explainability in AI
• Day 2: Feature Importance and Partial Dependence Plots
• Day 3: Model-agnostic Methods: LIME, SHAP
• Day 4: Interpreting Deep Learning Models: Activation Maps, Attention
• Day 5: Practical application and a mini-project