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Course Outline
Introduction to TensorFlow Lite
- Overview of TensorFlow Lite and its architecture
- Comparison with TensorFlow and other edge AI frameworks
- Benefits and challenges of using TensorFlow Lite for Edge AI
- Case studies of TensorFlow Lite in Edge AI applications
Setting Up the TensorFlow Lite Environment
- Installing TensorFlow Lite and its dependencies
- Configuring the development environment
- Introduction to TensorFlow Lite tools and libraries
- Hands-on exercises for environment setup
Developing AI Models with TensorFlow Lite
- Designing and training AI models for edge deployment
- Converting TensorFlow models to TensorFlow Lite format
- Optimizing models for performance and efficiency
- Hands-on exercises for model development and conversion
Deploying TensorFlow Lite Models
- Deploying models on various edge devices (e.g., smartphones, microcontrollers)
- Running inferences on edge devices
- Troubleshooting deployment issues
- Hands-on exercises for model deployment
Tools and Techniques for Model Optimization
- Quantization and its benefits
- Pruning and model compression techniques
- Utilizing TensorFlow Lite's optimization tools
- Hands-on exercises for model optimization
Building Practical Edge AI Applications
- Developing real-world Edge AI applications using TensorFlow Lite
- Integrating TensorFlow Lite models with other systems and applications
- Case studies of successful Edge AI projects
- Hands-on project for building a practical Edge AI application
Summary and Next Steps
Requirements
- An understanding of AI and machine learning concepts
- Experience with TensorFlow
- Basic programming skills (Python recommended)
Audience
- Developers
- Data scientists
- AI practitioners
14 Hours
