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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
- A solid grasp of AI and machine learning concepts.
- Prior experience working with TensorFlow.
- Fundamental programming competence (Python is recommended).
Target Audience
- Software Developers.
- Data Scientists.
- AI Practitioners.
14 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
