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Course Outline
Getting Started with TensorFlow Lite
- An overview of TensorFlow Lite’s architecture and core components
- A comparative analysis with TensorFlow and alternative edge AI frameworks
- Exploring the advantages and potential challenges of adopting TensorFlow Lite for Edge AI
- Examining real-world case studies of TensorFlow Lite in Edge AI contexts
Preparing the TensorFlow Lite Development Environment
- Installing TensorFlow Lite along with its necessary dependencies
- Setting up and configuring the development workspace
- Getting acquainted with the TensorFlow Lite toolkit and libraries
- Practical exercises focused on environment initialization
Creating AI Models with TensorFlow Lite
- Designing and training AI models specifically for edge deployment scenarios
- The process of converting standard TensorFlow models into the TensorFlow Lite format
- Strategies for optimizing model performance and operational efficiency
- Hands-on practice in model development and format conversion
Implementing TensorFlow Lite Model Deployment
- Deploying models across various edge hardware, such as smartphones and microcontrollers
- Executing inference processes directly on edge devices
- Diagnosing and resolving common deployment challenges
- Practical exercises dedicated to model deployment workflows
Advanced Tools and Techniques for Model Optimization
- Understanding quantization and its impact on model size and speed
- Applying pruning and other model compression methods
- Leveraging TensorFlow Lite’s built-in optimization utilities
- Hands-on sessions for executing model optimization techniques
Developing Practical Edge AI Solutions
- Building real-world Edge AI applications using TensorFlow Lite
- Integrating TensorFlow Lite models into broader system architectures and applications
- Reviewing case studies of successful Edge AI implementations
- A comprehensive hands-on project to construct a functional Edge AI application
Recap and Future Directions
Requirements
- A solid grasp of fundamental AI and machine learning principles
- Prior working experience with TensorFlow
- Foundational programming proficiency, with a recommendation for Python
Target Audience
- Software Developers
- Data Scientists
- AI Professionals
14 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
