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

  • An understanding of AI and machine learning concepts
  • Experience with TensorFlow
  • Basic programming skills (Python recommended)

Audience

  • Developers
  • Data scientists
  • AI practitioners
 14 Hours

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