Deep Learning and Neural Networks

  1. Deployment and Production
    1. Model Optimization for Inference
      1. Quantization Techniques
        1. Post-Training Quantization
          1. Quantization-Aware Training
            1. Precision Reduction Impact
            2. Model Pruning
              1. Structured Pruning
                1. Unstructured Pruning
                  1. Magnitude-Based Pruning
                    1. Gradual Pruning
                    2. Knowledge Distillation
                      1. Teacher-Student Framework
                        1. Soft Target Learning
                          1. Model Compression
                          2. Neural Architecture Optimization
                            1. Efficient Architectures
                              1. Hardware-Aware Design
                            2. Model Serving Infrastructure
                              1. Inference Servers
                                1. API Design and Implementation
                                  1. RESTful APIs
                                    1. gRPC Services
                                      1. GraphQL Interfaces
                                      2. Model Serialization
                                        1. Framework-Specific Formats
                                          1. ONNX Standard
                                            1. TensorFlow SavedModel
                                              1. PyTorch TorchScript
                                              2. Batch vs. Real-Time Inference
                                                1. Scalability Considerations
                                                  1. Load Balancing
                                                    1. Auto-Scaling
                                                      1. Caching Strategies
                                                    2. Performance Monitoring
                                                      1. Model Performance Metrics
                                                        1. Latency and Throughput Monitoring
                                                          1. Resource Utilization Tracking
                                                            1. Error Rate Analysis
                                                            2. Model Maintenance
                                                              1. Data Drift Detection
                                                                1. Statistical Tests
                                                                  1. Distribution Monitoring
                                                                  2. Model Drift Detection
                                                                    1. Performance Degradation
                                                                      1. Concept Drift
                                                                      2. Retraining Strategies
                                                                        1. Scheduled Retraining
                                                                          1. Trigger-Based Retraining
                                                                            1. Incremental Learning
                                                                            2. A/B Testing for Models
                                                                            3. MLOps Practices
                                                                              1. Version Control
                                                                                1. Model Versioning
                                                                                  1. Data Versioning
                                                                                    1. Experiment Tracking
                                                                                    2. Continuous Integration and Deployment
                                                                                      1. Automated Testing
                                                                                        1. Model Validation Pipelines
                                                                                          1. Deployment Automation
                                                                                          2. Reproducibility
                                                                                            1. Environment Management
                                                                                              1. Seed Setting
                                                                                                1. Deterministic Training
                                                                                                2. Collaboration and Workflow Management
                                                                                                  1. Team Collaboration Tools
                                                                                                    1. Workflow Orchestration
                                                                                                      1. Documentation Standards

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                                                                                                  10. Advanced Topics and Specialized Architectures

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                                                                                                  1. Foundations of Machine Learning and Neural Networks