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Course Outline
Introduction to TensorFlow Lite
- An overview of TensorFlow Lite and its architectural design
- Comparisons between TensorFlow Lite, TensorFlow, and other edge AI frameworks
- Advantages and challenges associated with using TensorFlow Lite for Edge AI
- Case studies demonstrating TensorFlow Lite in Edge AI applications
Setting Up the TensorFlow Lite Environment
- Installing TensorFlow Lite and its necessary dependencies
- Configuring the development environment
- Introduction to TensorFlow Lite tools and libraries
- Practical exercises for setting up the environment
Developing AI Models with TensorFlow Lite
- Designing and training AI models suitable for edge deployment
- Converting existing TensorFlow models to the TensorFlow Lite format
- Optimizing models for enhanced performance and efficiency
- Practical exercises focused on model development and conversion
Deploying TensorFlow Lite Models
- Deploying models across various edge devices (such as smartphones and microcontrollers)
- Executing inferences on edge devices
- Resolving deployment-related issues
- Practical exercises for model deployment
Tools and Techniques for Model Optimization
- Understanding quantization and its advantages
- Exploring pruning and model compression techniques
- Leveraging TensorFlow Lite's optimization tools
- Practical exercises for model optimization
Building Practical Edge AI Applications
- Creating 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 to build a practical Edge AI application
Summary and Next Steps
Requirements
- A solid grasp of AI and machine learning concepts
- Prior experience working with TensorFlow
- Foundational programming skills (Python is recommended)
Target Audience
- Developers
- Data scientists
- AI practitioners
14 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
