UsefulLinks
Computer Science
Artificial Intelligence
Deep Learning
Deep Learning and Neural Networks
1. Foundations of Machine Learning and Neural Networks
2. Training Shallow Neural Networks
3. Deepening the Network
4. Practical Considerations for Training
5. Convolutional Neural Networks (CNNs)
6. Recurrent Neural Networks (RNNs)
7. The Transformer Architecture
8. Generative Models
9. Deep Reinforcement Learning
10. Advanced Topics and Specialized Architectures
11. Deployment and Production
10.
Advanced Topics and Specialized Architectures
10.1.
Graph Neural Networks
10.1.1.
Graph Representation Learning
10.1.2.
Graph Convolutional Networks
10.1.3.
Graph Attention Networks
10.1.4.
Applications in Social Networks
10.2.
Neural Architecture Search
10.2.1.
Automated Architecture Design
10.2.2.
Search Strategies
10.2.3.
Performance Estimation
10.3.
Meta-Learning
10.3.1.
Learning to Learn
10.3.2.
Few-Shot Learning
10.3.3.
Model-Agnostic Meta-Learning
10.4.
Continual Learning
10.4.1.
Catastrophic Forgetting
10.4.2.
Lifelong Learning Strategies
10.4.3.
Memory-Based Approaches
10.5.
Federated Learning
10.5.1.
Distributed Training
10.5.2.
Privacy-Preserving Learning
10.5.3.
Communication Efficiency
10.6.
Interpretability and Explainability
10.6.1.
Model Interpretability Methods
10.6.2.
Attention Visualization
10.6.3.
Gradient-Based Explanations
10.6.4.
LIME and SHAP
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9. Deep Reinforcement Learning
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11. Deployment and Production