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Course Outline
Introduction
- TensforFlow Lite's game changing role in embedded systems and IoT
Overview of TensorFlow Lite Features and Operations
- Addressing limited device resources
- Default and expanded operations
Setting up TensorFlow Lite
- Installing the TensorFlow Lite interpreter
- Installing other TensorFlow packages
- Working from the command line vs Python API
Choosing a Model to Run on a Device
- Overview of pre-trained models: image classification, object detection, smart reply, pose estimation, segmentation
- Choosing a model from TensorFlow Hub or other source
Customizing a Pre-trained Model
- How transfer learning works
- Retraining an image classification model
Converting a Model
- Understanding the TensorFlow Lite format (size, speed, optimizations, etc.)
- Converting a model to the TensorFlow Lite format
Running a Prediction Model
- Understanding how the model, interpreter, input data work together
- Calling the interpreter from a device
- Running data through the model to obtain predictions
Accelerating Model Operations
- Understanding on-board acceleration, GPUs, etc.
- Configuring Delegates to accelerate operations
Adding Model Operations
- Using TensorFlow Select to add operations to a model.
- Building a custom version of the interpreter
- Using Custom operators to write or port new operations
Optimizing the Model
- Understanding the balance of performance, model size, and accuracy
- Using the Model Optimization Toolkit to optimize the size and performance of a model
- Post-training quantization
Troubleshooting
Summary and Conclusion
Requirements
- An understanding of deep learning concepts
- Python programming experience
- A device running embedded Linux (Raspberry Pi, Coral device, etc.)
Audience
- Developers
- Data scientists with an interest in embedded systems
21 Hours
Testimonials (1)
Maybe more exercises could be better for lerning but the time was to little
