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Sobre este cursoOmitir Sobre este curso
Have you wanted to build a TinyML device? In Deploying TinyML, you will learn the software, write the code, and deploy the model to your own tiny microcontroller-based device. Before you know it, you’ll be implementing an entire TinyML application.
A one-of-a-kind course, Deploying TinyML is a mix of computer science and electrical engineering. Gain hands-on experience with embedded systems, machine learning training, and machine learning deployment using TensorFlow Lite for Microcontrollers, to make your own microcontroller operational for implementing applications such as voice recognition, sound detection, and gesture detection.
The course features projects based on a TinyML Program Kit that includes an Arduino board with onboard sensors and an ARM Cortex-M4 microcontroller. The kit has everything you need to build applications around image recognition, audio processing, and gesture detection. Before you know it, you’ll be implementing an entire tiny machine learning application. You can preorder your Arduino kit here.
Tiny Machine Learning (TinyML) is one of the fastest-growing areas of deep learning and is rapidly becoming more accessible. The third course in the TinyML Professional Certificate program, Deploying TinyML provides hands-on experience with deploying TinyML to a physical device.
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Lo que aprenderásOmitir Lo que aprenderás
- An understanding of the hardware of a microcontroller-based device
- A review of the software behind a microcontroller-based device
- How to program your own TinyML device
- How to write your code for a microcontroller-based device
- How to deploy your code to a microcontroller-based device
- How to train a microcontroller-based device
- Responsible AI Deployment
Plan de estudiosOmitir Plan de estudios
- Introduction to the TinyML Kit
- Deploying TinyML Applications on Embedded Devices
- Collecting a Custom TinyML Dataset
- Pre and Post Processing for Keyword Spotting, Visual Wake Words, and Gesturing a Magic Wand
- Profiling and Optimization of TinyML Applications