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Course Outline
Introduction to TensorFlow Lite
- An overview of TensorFlow Lite and its architecture.
- Comparing TensorFlow Lite with standard TensorFlow and other edge AI frameworks.
- Exploring the benefits and challenges of using TensorFlow Lite for Edge AI.
- Case studies showcasing TensorFlow Lite in Edge AI applications.
Setting Up the TensorFlow Lite Environment
- Installing TensorFlow Lite and required dependencies.
- Configuring the development environment.
- Introduction to TensorFlow Lite tools and libraries.
- Practical exercises for environment setup.
Developing AI Models with TensorFlow Lite
- Designing and training AI models suitable for edge deployment.
- Converting existing TensorFlow models into the TensorFlow Lite format.
- Optimizing models for enhanced performance and efficiency.
- Hands-on exercises for model development and conversion.
Deploying TensorFlow Lite Models
- Deploying models on diverse edge devices (e.g., smartphones, microcontrollers).
- Executing inferences on edge devices.
- Troubleshooting common deployment issues.
- Hands-on exercises for model deployment.
Tools and Techniques for Model Optimization
- Understanding quantization and its advantages.
- Techniques for pruning and model compression.
- Leveraging 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.
- A hands-on project for building a practical Edge AI application.
Summary and Next Steps
Requirements
- A solid understanding of artificial intelligence and machine learning concepts.
- Prior experience with TensorFlow.
- Fundamental programming proficiency (Python is recommended).
Target Audience
- Software developers.
- Data scientists.
- AI specialists.
14 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
