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Duration 14 hours
Course Outline
Introduction to TensorFlow Lite
- Architectural overview of TensorFlow Lite
- Distinctions between TensorFlow Lite, TensorFlow, and other edge AI frameworks
- Advantages and considerations when adopting TensorFlow Lite for Edge AI
- Real-world case studies demonstrating TensorFlow Lite in Edge AI scenarios
Preparing the TensorFlow Lite Environment
- Installing TensorFlow Lite alongside its necessary dependencies
- Configuring the development workspace
- Overview of essential TensorFlow Lite tools and libraries
- Practical exercises focused on environment configuration
AI Model Development with TensorFlow Lite
- Designing and training AI models suitable for edge implementation
- Transforming TensorFlow models into the TensorFlow Lite format
- Enhancing model performance and resource efficiency
- Hands-on tasks for model creation and format conversion
Deploying TensorFlow Lite Models
- Implementing models on diverse edge platforms, including smartphones and microcontrollers
- Executing inference processes on edge hardware
- Diagnosing and resolving common deployment challenges
- Practical exercises in model deployment workflows
Tools and Strategies for Model Optimization
- Understanding quantization and its performance benefits
- Applying pruning and model compression methodologies
- Leveraging native TensorFlow Lite optimization utilities
- Hands-on practice in optimizing model architecture
Creating Practical Edge AI Applications
- Building real-world Edge AI solutions using TensorFlow Lite
- Integrating TensorFlow Lite models into broader system architectures
- Analyzing successful Edge AI project case studies
- A hands-on project dedicated to constructing a functional Edge AI application
Conclusion and Future Directions
Requirements
- A foundational understanding of AI and machine learning principles
- Prior experience working with TensorFlow
- Basic coding proficiency (Python is highly recommended)
Target Audience
- Software Developers
- Data Scientists
- AI Practitioners
Testimonials (1)
That we can cover advance topic and work with real-life example
