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Course Outline
Introduction to TensorFlow Lite
- Overview of TensorFlow Lite architecture
- Comparison with TensorFlow and alternative edge AI frameworks
- Advantages and challenges of adopting TensorFlow Lite for Edge AI
- Case studies showcasing TensorFlow Lite in Edge AI contexts
Establishing the TensorFlow Lite Environment
- Installing TensorFlow Lite along with its dependencies
- Configuring the development workspace
- Introduction to TensorFlow Lite utilities and libraries
- Practical exercises for setting up the environment
Crafting AI Models with TensorFlow Lite
- Designing and training AI models for edge deployment
- Converting TensorFlow models into the TensorFlow Lite format
- Optimizing models for improved performance and efficiency
- Hands-on exercises focused on model development and conversion
Deploying TensorFlow Lite Models
- Deploying models onto various edge devices (e.g., smartphones, microcontrollers)
- Executing inferences on edge devices
- Resolving common deployment challenges
- Hands-on exercises for model deployment
Tools and Techniques for Model Optimization
- Quantization and its advantages
- Pruning and model compression strategies
- Leveraging optimization tools provided by TensorFlow Lite
- Hands-on exercises for model optimization
Constructing Practical Edge AI Applications
- Developing real-world Edge AI applications with TensorFlow Lite
- Integrating TensorFlow Lite models with other systems and applications
- Case studies of successful Edge AI projects
- Hands-on project to build a practical Edge AI application
Summary and Next Steps
Requirements
- Foundational knowledge of AI and machine learning concepts
- Prior experience working with TensorFlow
- Essential programming proficiency (Python is recommended)
Target Audience
- Developers
- Data scientists
- AI professionals
14 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
