Deploying TinyML, Harvard Online Course
TinyML: Learn programing in TensorFlow Lite for microcontrollers, write the code, and deploy your model to your very own tiny microcontroller.
Introduction
Learn to program in TensorFlow Lite for microcontrollers so that you can write the code, and deploy your model to your very own tiny microcontroller. Before you know it, you’ll be implementing an entire TinyML application.
About this course
Have you wanted to build a Tiny Machine Learning (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 Tiny Machine Learning 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 is one of the fastest-growing areas of deep learning and is rapidly becoming more accessible. The third course in the Tiny Machine Learning Professional Certificate program, Deploying TinyML provides hands-on experience with deploying TinyML to a physical device.
What you will learn from Deploying TinyML?
- 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.
- 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.
Prerequisites
- Applications of Tiny Machine Learning
- Basic Programming in C/C++
- TinyML Course Kit
Syllabus
- Introduction to the TinyML Kit
- Deploying Tiny Machine Learning 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
About the Chapter
1.1: Welcome to Deploying TinyML
1.2: Getting Started
1.2: TEST (Getting Started)
1.3: Embedded Hardware and Software
1.3: TEST (Embedded Hardware and Software)
1.4: TensorFlow Lite Micro
1.4: TEST (TensorFlow Lite Micro)
1.5: Keyword Spotting
1.5: TEST (Keyword Spotting)
1.6: Custom Dataset Engineering for Keyword Spotting
1.6: TEST (Custom Dataset Engineering for Keyword Spotting)
1.7: Visual Wake Words
1.7: TEST (Visual Wake Words)
1.8: Gesturing Magic Wand
1.8: TEST (Gesturing Magic Wand)
1.9: Responsible AI Deployment
1.9: TEST (Responsible AI Deployment)
1.10: Summary
Note: Your review matters
If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.
FAQ
Description
Introduction
Learn to program in TensorFlow Lite for microcontrollers so that you can write the code, and deploy your model to your very own tiny microcontroller. Before you know it, you’ll be implementing an entire TinyML application.
About this course
Have you wanted to build a Tiny Machine Learning (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 Tiny Machine Learning 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 is one of the fastest-growing areas of deep learning and is rapidly becoming more accessible. The third course in the Tiny Machine Learning Professional Certificate program, Deploying TinyML provides hands-on experience with deploying TinyML to a physical device.
What you will learn from Deploying TinyML?
- 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.
- 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.
Prerequisites
- Applications of Tiny Machine Learning
- Basic Programming in C/C++
- TinyML Course Kit
Syllabus
- Introduction to the TinyML Kit
- Deploying Tiny Machine Learning 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
About the Chapter
1.1: Welcome to Deploying TinyML
1.2: Getting Started
1.2: TEST (Getting Started)
1.3: Embedded Hardware and Software
1.3: TEST (Embedded Hardware and Software)
1.4: TensorFlow Lite Micro
1.4: TEST (TensorFlow Lite Micro)
1.5: Keyword Spotting
1.5: TEST (Keyword Spotting)
1.6: Custom Dataset Engineering for Keyword Spotting
1.6: TEST (Custom Dataset Engineering for Keyword Spotting)
1.7: Visual Wake Words
1.7: TEST (Visual Wake Words)
1.8: Gesturing Magic Wand
1.8: TEST (Gesturing Magic Wand)
1.9: Responsible AI Deployment
1.9: TEST (Responsible AI Deployment)
1.10: Summary
Note: Your review matters
If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.
FAQ
Specification:
- EDX
- Harvard University
- Online Course
- Self-paced
- Intermediate
- 1-3 Months
- Free Course (Affordable Certificate)
- English
- Artificial intelligence Data Science Data Science with 'Python' Deep learning Machine learning Microcontrollers TensorFlow TinyML
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