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Course Outline
Introduction to TensorFlow Lite
- A comprehensive look at TensorFlow Lite architecture and capabilities.
- Comparative analysis with TensorFlow and competing edge AI frameworks.
- Exploring the advantages and operational challenges of TensorFlow Lite in Edge AI.
- Reviewing case studies highlighting TensorFlow Lite in real-world Edge AI contexts.
Configuring the TensorFlow Lite Workspace
- Installing TensorFlow Lite along with necessary dependencies.
- Setting up and configuring the development environment.
- An introduction to the core tools and libraries within the TensorFlow Lite suite.
- Practical exercises focused on environment setup.
Building AI Models with TensorFlow Lite
- Designing and training AI models specifically for edge deployment.
- Converting standard TensorFlow models into the TensorFlow Lite format.
- Enhancing model performance and efficiency through optimization.
- Hands-on tasks for model development and conversion.
Deploying TensorFlow Lite Models
- Executing model deployment on various edge platforms, such as smartphones and microcontrollers.
- Managing inference processes on edge devices.
- Diagnosing and resolving common deployment obstacles.
- Practical exercises dedicated to model deployment strategies.
Advanced Tools and Techniques for Model Optimization
- Understanding quantization and its positive impact on model size and speed.
- Applying pruning and other model compression techniques.
- Leveraging built-in optimization tools within TensorFlow Lite.
- Practical exercises for optimizing model performance.
Creating Practical Edge AI Applications
- Developing real-world Edge AI solutions powered by TensorFlow Lite.
- Integrating TensorFlow Lite models with external systems and broader applications.
- Analyzing successful Edge AI project case studies.
- A hands-on project to build a complete, practical Edge AI application.
Summary and Future Directions
Requirements
- A solid foundational understanding of AI and machine learning principles.
- Prior hands-on experience with the TensorFlow framework.
- Competent programming skills, with proficiency in Python strongly recommended.
Target Audience
- Software developers.
- Data scientists.
- AI practitioners.
14 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
