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Applications Kit ML101 - tinyML Starter Kit with Prof. Vijay

Seeed Studio  |  SKU: 4584
£69.99
Tax included, shipping and discounts calculated at checkout.
DESCRIPTION

"Tiny Machine Learning (TinyML) is a fast-growing and emerging technological field at the intersection of machine learning (ML) algorithms and low-cost embedded systems. Indeed, TinyML has a myriad of applications, ranging from predictive maintenance in manufacturing, to enabling customized voice interfaces for everyday life consumer devices, life coffee maker, toaster, or oven. Furthermore, TinyML enables on-device analysis of rich sensor data to allow an expansive, new array of always-on ML use cases at the endpoint.

Therefore this Wio Terminal Kit has everything you need to get started with TinyML. Join me in this journey as I take you through the fundamentals of TinyML using the Wio Terminal. Together, we can pave the future of TinyML"

Prof. Vijay Janapa Reddi

 

With the motivation of facilitating a bigger population - regardless of their backgrounds - to learn and apply ultra-low power machine learning at the edge to solve the industrial, environmental, social, and economic concerns of our time, Seeed are honoured and excited to collaborate with Professor Vijay Janapa Reddi of Harvard University on this initiative with generous support from tinyML Foundation and Edge Impulse to create this Start Kit and accompanying course. 

 

Hardware Included - Wio Terminal & Grove

This kit includes 1 x Wio Terminal and 2 x Grove sensors:  

Wio Terminal is compatible with Arduino and Micropython, built with an ATSAMD51 microcontroller with wireless connectivity supported by Realtek RTL8720DN. Its CPU speed runs at 120MHz (Boost up to 200MHz). Realtek RTL8720DN chip supports both Bluetooth and Wi-Fi providing the backbone for IoT projects. The Wio Terminal is Highly Integrated with a 2.4” LCD Screen, there is an onboard IMU(LIS3DHTR), microphone, buzzer, microSD card slot, light sensor, and infrared emitter(IR 940nm). 

Grove is an open source, modular, easy-to-use toolset optimized for simplicity. The 2 Grove sensors included in the kit are Grove Ultrasonic sensor, Grove Temperature sensor & Humidity sensor. 

With the hardware kit, student experiences, and curated curriculum, you can get hands-on experience and learn about the full circle of Machine Learning algorithms (Data Collection, Pre-processing, Feature Extraction, Model Training, Model Optimizations, ML Model Deployment) whether you are in the classroom, at home, or through distance learning courses, and then you can apply the knowledge to build edge machine learning projects in the real world. 

Course - developed by Prof. Vijay Reddi & Seeed Edu team

Vijay Janapa Reddi is an Associate Professor at Harvard University, Inference Co-chair for MLPerf, and a founding member of MLCommons, a nonprofit ML (Machine Learning) organization aiming to accelerate ML innovation.

A little preview of the course:

Who’s This Book For:

This book is designed specifically for educators to be able to adopt the Wio Terminal into the classroom or workshops to show learners the power of edge machine learning. It provides the basic underpinnings that one would have to cover to teach the very basics of ML while keeping the concepts grounded in hands-on exercises. 

Course Structure:

This book is specifically designed to serve as a hands-on booklet for teachers and learners to get started with edge machine learning. Ideally, one would be able to learn the concepts from this book and be able to teach the fundamental concepts of applied machine learning. The keyword is applied as this course focuses on the application of machine learning concepts, rather than on the technical and theoretical aspects of machine learning. 

What You’ll Learn:

Users of this book will learn how to train and deploy deep neural network models on Cortex-M core microcontroller devices from Seeed Studio. Course content features detailed step-by-step projects that will allow students to grasp basic ideas about modern Machine Learning and how it can be used in low-power and footprint microcontrollers to create intelligent and connected systems.  

After completing the course, the students will be able to design and implement their own Machine Learning enabled projects on Cortex-M core microcontrollers, starting from defining a problem to gathering data and training the neural network model and finally deploying it to the device to display inference results or control other hardware appliances based on inference data. Course content is based on using the Edge Impulse platform, which simplifies data collection/ model training/ conversion pipeline. 

Get full access to the course by scanning the QR code on the box.


FEATURES
  • Highly integrated kit: microphone, 3-axis accelerometer, light sensor, 2.4’’ LCD screen, 5-way switch, 1x Grove Ultrasonic sensor, 1x Grove Temperature & Humidity sensor
  • Course-in-a-box: exclusive course developed by a professor from Harvard University
  • From theory to practice: introduces fundamental theory to actual applications of ultra-low power machine learning at the edge
  • Extensive use: supports Codecraft graphical programming, Arduino, Micropython, etc
RESOURCES
Applications Kit ML101 - tinyML Starter Kit with Prof. Vijay
Seeed Studio

Applications Kit ML101 - tinyML Starter Kit with Prof. Vijay

£69.99
DESCRIPTION

"Tiny Machine Learning (TinyML) is a fast-growing and emerging technological field at the intersection of machine learning (ML) algorithms and low-cost embedded systems. Indeed, TinyML has a myriad of applications, ranging from predictive maintenance in manufacturing, to enabling customized voice interfaces for everyday life consumer devices, life coffee maker, toaster, or oven. Furthermore, TinyML enables on-device analysis of rich sensor data to allow an expansive, new array of always-on ML use cases at the endpoint.

Therefore this Wio Terminal Kit has everything you need to get started with TinyML. Join me in this journey as I take you through the fundamentals of TinyML using the Wio Terminal. Together, we can pave the future of TinyML"

Prof. Vijay Janapa Reddi

 

With the motivation of facilitating a bigger population - regardless of their backgrounds - to learn and apply ultra-low power machine learning at the edge to solve the industrial, environmental, social, and economic concerns of our time, Seeed are honoured and excited to collaborate with Professor Vijay Janapa Reddi of Harvard University on this initiative with generous support from tinyML Foundation and Edge Impulse to create this Start Kit and accompanying course. 

 

Hardware Included - Wio Terminal & Grove

This kit includes 1 x Wio Terminal and 2 x Grove sensors:  

Wio Terminal is compatible with Arduino and Micropython, built with an ATSAMD51 microcontroller with wireless connectivity supported by Realtek RTL8720DN. Its CPU speed runs at 120MHz (Boost up to 200MHz). Realtek RTL8720DN chip supports both Bluetooth and Wi-Fi providing the backbone for IoT projects. The Wio Terminal is Highly Integrated with a 2.4” LCD Screen, there is an onboard IMU(LIS3DHTR), microphone, buzzer, microSD card slot, light sensor, and infrared emitter(IR 940nm). 

Grove is an open source, modular, easy-to-use toolset optimized for simplicity. The 2 Grove sensors included in the kit are Grove Ultrasonic sensor, Grove Temperature sensor & Humidity sensor. 

With the hardware kit, student experiences, and curated curriculum, you can get hands-on experience and learn about the full circle of Machine Learning algorithms (Data Collection, Pre-processing, Feature Extraction, Model Training, Model Optimizations, ML Model Deployment) whether you are in the classroom, at home, or through distance learning courses, and then you can apply the knowledge to build edge machine learning projects in the real world. 

Course - developed by Prof. Vijay Reddi & Seeed Edu team

Vijay Janapa Reddi is an Associate Professor at Harvard University, Inference Co-chair for MLPerf, and a founding member of MLCommons, a nonprofit ML (Machine Learning) organization aiming to accelerate ML innovation.

A little preview of the course:

Who’s This Book For:

This book is designed specifically for educators to be able to adopt the Wio Terminal into the classroom or workshops to show learners the power of edge machine learning. It provides the basic underpinnings that one would have to cover to teach the very basics of ML while keeping the concepts grounded in hands-on exercises. 

Course Structure:

This book is specifically designed to serve as a hands-on booklet for teachers and learners to get started with edge machine learning. Ideally, one would be able to learn the concepts from this book and be able to teach the fundamental concepts of applied machine learning. The keyword is applied as this course focuses on the application of machine learning concepts, rather than on the technical and theoretical aspects of machine learning. 

What You’ll Learn:

Users of this book will learn how to train and deploy deep neural network models on Cortex-M core microcontroller devices from Seeed Studio. Course content features detailed step-by-step projects that will allow students to grasp basic ideas about modern Machine Learning and how it can be used in low-power and footprint microcontrollers to create intelligent and connected systems.  

After completing the course, the students will be able to design and implement their own Machine Learning enabled projects on Cortex-M core microcontrollers, starting from defining a problem to gathering data and training the neural network model and finally deploying it to the device to display inference results or control other hardware appliances based on inference data. Course content is based on using the Edge Impulse platform, which simplifies data collection/ model training/ conversion pipeline. 

Get full access to the course by scanning the QR code on the box.


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