Diploma in On-Device Runtimes (TFLite/ONNX/TensorRT)

9.9 Basic Propulsion Concepts
9.9 Rotor Aerodynamics: Lift, Drag, and Efficiency
9.3 Rotor Types and Configurations
9.4 Rotorcraft Stability and Control
9.5 Applicable Aviation Legislation
9.6 Safety and Operational Regulations
9.7 Aircraft Design and Regulations
9.8 The Role of ICAO and FAA
9.9 Risk Factors in Aviation
9.90 Certification and Airworthiness Requirements

9.9 Mathematical Modeling of Rotors
9.9 Finite Element Analysis Methods
9.3 Computational Fluid Dynamics (CFD) Simulation
9.4 Static and Dynamic Performance Analysis
9.5 Modeling of Flight Control Systems
9.6 Design and Performance Parameters
9.7 Rotor Modeling Software
9.8 Model Validation and Verification
9.9 Practical Modeling Case Studies
9.90 Rotor Design Optimization

3.9 In-Flight Performance Analysis 3.9 Cruise Flight Performance Analysis
3.3 Climb and Descent Flight Performance Analysis
3.4 Influence of Environmental Conditions
3.5 Stability and Control Analysis
3.6 Wind Tunnel and Flight Testing
3.7 Data Analysis Methodologies
3.8 Interpretation of Results and Conclusions
3.9 Failure Analysis and Safety
3.90 Rotor Performance Optimization

4.9 Optimization for Different Hardware
4.9 Quantization Techniques
4.3 Model Compression
4.4 Specific Optimization Strategies for TFLite, ONNX, and TensorRT
4.5 Hardware and Platform Selection
4.6 Profiling and Performance Analysis Tools
4.7 Performance Evaluation and Key Metrics
4.8 Fine-Tuning of Optimized Models
4.9 Power and Consumption Considerations
4.90 Implementation on Specific Devices

5.9 Implementation Workflows
5.9 Integration of Optimized Models into Applications
5.3 Tuning and Adjustment Techniques Real-time fine-tuning
5.4 Security and reliability considerations
5.5 Exception and error handling
5.6 Implementation in different programming languages
5.7 On-device testing and validation
5.8 Real-time performance monitoring
5.9 Model updates and maintenance
5.90 Deploying and managing models in production

6.9 Performance considerations on mobile devices
6.9 Optimization for CPU, GPU, and accelerators
6.3 Model size reduction strategies
6.4 Memory optimization techniques
6.5 User interface design for mobile devices
6.6 Performance evaluation on mobile devices
6.7 Battery and power consumption testing
6.8 Implementation on iOS and Android
6.9 Comparative analysis of different devices
6.90 Success stories

7.9 Introduction to model optimization
7.9 TFLite fundamentals
7.3 ONNX fundamentals
7.4 TensorRT fundamentals
7.5 Quantization techniques
7.6 Model Compression and Pruning
7.7 Optimized Model Architectures
7.8 Tools and Workflows
7.9 Performance Evaluation
7.90 Hardware Considerations

8.9 Advanced Optimization in TFLite
8.9 Advanced Optimization in ONNX
8.3 Advanced Optimization in TensorRT
8.4 Hardware-Specific Optimization Techniques
8.5 Memory and Latency Optimization
8.6 Power Consumption Optimization
8.7 Fine-Tuning and Calibration Techniques
8.8 Deploying Optimized Models
8.9 Performance Monitoring and Analysis
8.90 Model Deployment and Management

9.9 Introduction to TensorFlow Lite
9.9 Introduction to ONNX
9.3 Introduction to TensorRT
9.4 Conversion Workflows
9.5 Model Optimization Tools
9.6 Performance and Accuracy Comparison
9.7 Selecting the Right Tool
9.8 Integration into Projects
9.9 Hardware Considerations
9.90 Best Practices