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
Testimonials (5)
Practical excersises
Marcin Janicki - Hectronic Polska Sp. z o.o.
Course - Yocto Project
That the trainer adapts to our needs
Eduardo Fontecha - ORMAZABAL PROTECTION & AUTOMATION S.L.U.
Course - The Yocto Project - An Overview - hands-on
Maybe more exercises could be better for lerning but the time was to little
Gianpiero Arico' - Urmet Spa
Course - Embedded Linux Systems Architecture
The knowledge of the trainer. He was able to answer all of my questions, even questions about our platform. He also continued to help until we all understood the material.
James O'Donnell - Tennant Company
Course - Embedded Linux Kernel and Driver Development
I understood the process of the operating system and how do we link all factors together information of network as well so now I have an obvious and full picture about what is going on these computers how they communicate with each others ultimately gained knowledge about the most important operating system which is Linux and how do we implement our own embedded Linux