Deploying TinyML, Harvard Online Course

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TinyML: Learn programing in TensorFlow Lite for microcontrollers, write the code, and deploy your model to your very own tiny microcontroller.

Last updated on December 3, 2021 1:27 am

Introduction

Learn to configure in TensorFlow Lite for microcontrollers to make sure that you can compose the code, and also release your version to your extremely own tiny microcontroller. Before you understand it, you’ll be executing a whole TinyML application.

About this course

Have you wished to construct a Tiny Machine Learning (TinyML) gadget? In Deploying TinyML, you will certainly discover the software program, compose the code, and also release the version to your very own tiny microcontroller-based gadget. Before you understand it, you’ll be executing a whole TinyML application.

A one-of-a-kind course, Deploying TinyML is a mix of computer technology and also electric engineering. Gain hands-on experience with embedded systems, machine learning training, and also machine learning implementation utilizing TensorFlow Lite for Microcontrollers, to make your very own microcontroller is functional for executing applications such as voice recognition, audio detection, and also motion/gesture detection.

The course includes projects based upon a Tiny Machine Learning Program Kit that consists of an Arduino board with onboard sensing units and also an ARM Cortex-M4 microcontroller. The package has every little thing you require to construct applications around photo recognition, sound processing, and also motion/gesture discovery.

Before you understand it, you’ll be executing whole small equipment discovering application. You can preorder your Arduino package right here.

Tiny Machine Learning is just one of the fastest-growing areas of deep learning and also is quickly becoming easily accessible. The 3rd course in the Tiny Machine Learning Professional Certificate program, Deploying TinyML offers hands-on experience with deploying TinyML to a physical gadget.

What you will learn from Deploying TinyML?

  • Understanding of the equipment of a microcontroller-based gadget.
  • Evaluation of the software program behind a microcontroller-based gadget.
  • How to configure your very own TinyML gadget.
  • To compose your code for a microcontroller-based gadget.
  • How to release your code to a microcontroller-based gadget.
  • How to educate a microcontroller-based gadget.
  • 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 also Post Processing for Keyword Spotting, Visual Wake Words, and also Gesturing a Magic Wand
  • Profiling and also Optimization of TinyML Applications

About the Chapters

1.1: Welcome to Deploying TinyML

1.2: Getting Started

1.3: Embedded Hardware and also Software

1.4: TensorFlow Lite Micro

1.5: Keyword Spotting

1.6: Custom Dataset Engineering for Keyword Spotting

1.7: Visual Wake Words

1.8: Gesturing Magic Wand

1.9: Responsible AI Deployment

1.10: Summary

Learn more on TinyML:

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.

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Verified Certificate at

$199.00

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  • EDX
  • Harvard University
  • Online Course
  • Self-paced
  • Intermediate
  • 1-3 Months
  • Free Course (Affordable Certificate)
  • English
  • C/C++
  • Applications of TinyML Basic Programming in C/C++ TinyML Course Kit
  • Artificial intelligence Data Science Data Science with 'Python' Deep learning Machine learning Microcontrollers TensorFlow TinyML
PROS: Gain hands-on experience with embedded systems, machine learning training, and machine learning deployment. The course features hands-on projects based on a TinyML Program Kit. Kit plays a vital role in this course. Also, they have provided a facility to preorder your Arduino kit here.
CONS: Need to carry TinyML Course Kit. Need more elaboration on some course topics.

Description

Introduction

Learn to configure in TensorFlow Lite for microcontrollers to make sure that you can compose the code, and also release your version to your extremely own tiny microcontroller. Before you understand it, you’ll be executing a whole TinyML application.

About this course

Have you wished to construct a Tiny Machine Learning (TinyML) gadget? In Deploying TinyML, you will certainly discover the software program, compose the code, and also release the version to your very own tiny microcontroller-based gadget. Before you understand it, you’ll be executing a whole TinyML application.

A one-of-a-kind course, Deploying TinyML is a mix of computer technology and also electric engineering. Gain hands-on experience with embedded systems, machine learning training, and also machine learning implementation utilizing TensorFlow Lite for Microcontrollers, to make your very own microcontroller is functional for executing applications such as voice recognition, audio detection, and also motion/gesture detection.

The course includes projects based upon a Tiny Machine Learning Program Kit that consists of an Arduino board with onboard sensing units and also an ARM Cortex-M4 microcontroller. The package has every little thing you require to construct applications around photo recognition, sound processing, and also motion/gesture discovery.

Before you understand it, you’ll be executing whole small equipment discovering application. You can preorder your Arduino package right here.

Tiny Machine Learning is just one of the fastest-growing areas of deep learning and also is quickly becoming easily accessible. The 3rd course in the Tiny Machine Learning Professional Certificate program, Deploying TinyML offers hands-on experience with deploying TinyML to a physical gadget.

What you will learn from Deploying TinyML?

  • Understanding of the equipment of a microcontroller-based gadget.
  • Evaluation of the software program behind a microcontroller-based gadget.
  • How to configure your very own TinyML gadget.
  • To compose your code for a microcontroller-based gadget.
  • How to release your code to a microcontroller-based gadget.
  • How to educate a microcontroller-based gadget.
  • 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 also Post Processing for Keyword Spotting, Visual Wake Words, and also Gesturing a Magic Wand
  • Profiling and also Optimization of TinyML Applications

About the Chapters

1.1: Welcome to Deploying TinyML

1.2: Getting Started

1.3: Embedded Hardware and also Software

1.4: TensorFlow Lite Micro

1.5: Keyword Spotting

1.6: Custom Dataset Engineering for Keyword Spotting

1.7: Visual Wake Words

1.8: Gesturing Magic Wand

1.9: Responsible AI Deployment

1.10: Summary

Learn more on TinyML:

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.

Specification:

  • EDX
  • Harvard University
  • Online Course
  • Self-paced
  • Intermediate
  • 1-3 Months
  • Free Course (Affordable Certificate)
  • English
  • C/C++
  • Applications of TinyML Basic Programming in C/C++ TinyML Course Kit
  • Artificial intelligence Data Science Data Science with 'Python' Deep learning Machine learning Microcontrollers TensorFlow TinyML

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Deploying TinyML, Harvard Online Course
Deploying TinyML, Harvard Online Course

$199.00

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