Applications of TinyML, Course by Harvard
Get the opportunity to see TinyML in practice. You will see its examples, applications, and learn first-hand how to train these models for tiny applications
Introduction
Get the opportunity to see TinyML in practice. You will see its examples of applications, and learn first-hand how to train these models for tiny applications such as keyword spotting, visual wake words, and gesture recognition.
About this course
Do you know what happens when you say OK Google? to a Google device? Is your Google Home always listening?
Following on the Foundations of this course, Applications of TinyML will give you the opportunity to see tiny machine learning applications in practice. This course features real-world case studies, guided by industry leaders, that examine deployment challenges on tiny or deeply embedded devices.
Dive into the code for using sensor data for tasks such as gesture detection and voice recognition. Focusing on the neural network of the applications, specifically on training and inference, you will review the code behind OK Google,? Alexa,? and smartphone features on Android and Apple. Learn about real-world industry applications as well as Keyword Spotting, Visual Wake Words, Anomaly Detection, Dataset Engineering, and Responsible Artificial Intelligence.
Tiny Machine Learning (TinyML) is one of the fastest-growing areas of deep learning and is rapidly becoming more accessible. The second course in this Professional Certificate program, Applications of TinyML shows you the code behind some of the world’s most widely-used TinyML devices.
What you will learn from this course on TinyML?
- The code behind some of the most widely used applications of TinyML.
- Real-word industry applications.
- Principles of Keyword Spotting.
- Principles of Visual Wake Words.
- Concept of Anomaly Detection.
- Principles of Dataset Engineering.
- Responsible AI Development.
Prerequisites
- Fundamentals of this course or sufficient relevant experience:
- Basic Scripting in Python
- Basic usage of Colab
- Basics of Machine Learning
- Basics of Embedded Systems
Syllabus
Chapter_1.1: Welcome to Applications of TinyML
Chapter_1.2: TinyML: AI Lifecycle and ML Workflow
Chapter_1.3: Machine Learning on Mobile and Edge IoT Devices – Part 1
Chapter_1.4: Machine Learning on Mobile and Edge IoT Devices – Part 2
Chapter_1.5: TinyML: Keyword Spotting
Chapter_1.6: Data Engineering for TinyML Applications
Chapter_1.7: Visual Wake Words
Chapter_1.8: Anomaly Detection
Chapter_1.9: Responsible AI Development
Chapter_1.10: Summary
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Description
Introduction
Get the opportunity to see TinyML in practice. You will see its examples of applications, and learn first-hand how to train these models for tiny applications such as keyword spotting, visual wake words, and gesture recognition.
About this course
Do you know what happens when you say OK Google? to a Google device? Is your Google Home always listening?
Following on the Foundations of this course, Applications of TinyML will give you the opportunity to see tiny machine learning applications in practice. This course features real-world case studies, guided by industry leaders, that examine deployment challenges on tiny or deeply embedded devices.
Dive into the code for using sensor data for tasks such as gesture detection and voice recognition. Focusing on the neural network of the applications, specifically on training and inference, you will review the code behind OK Google,? Alexa,? and smartphone features on Android and Apple. Learn about real-world industry applications as well as Keyword Spotting, Visual Wake Words, Anomaly Detection, Dataset Engineering, and Responsible Artificial Intelligence.
Tiny Machine Learning (TinyML) is one of the fastest-growing areas of deep learning and is rapidly becoming more accessible. The second course in this Professional Certificate program, Applications of TinyML shows you the code behind some of the world’s most widely-used TinyML devices.
What you will learn from this course on TinyML?
- The code behind some of the most widely used applications of TinyML.
- Real-word industry applications.
- Principles of Keyword Spotting.
- Principles of Visual Wake Words.
- Concept of Anomaly Detection.
- Principles of Dataset Engineering.
- Responsible AI Development.
Prerequisites
- Fundamentals of this course or sufficient relevant experience:
- Basic Scripting in Python
- Basic usage of Colab
- Basics of Machine Learning
- Basics of Embedded Systems
Syllabus
Chapter_1.1: Welcome to Applications of TinyML
Chapter_1.2: TinyML: AI Lifecycle and ML Workflow
Chapter_1.3: Machine Learning on Mobile and Edge IoT Devices – Part 1
Chapter_1.4: Machine Learning on Mobile and Edge IoT Devices – Part 2
Chapter_1.5: TinyML: Keyword Spotting
Chapter_1.6: Data Engineering for TinyML Applications
Chapter_1.7: Visual Wake Words
Chapter_1.8: Anomaly Detection
Chapter_1.9: Responsible AI Development
Chapter_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
- Python
- Google Colab
- Basic Scripting in Python Colab Basics Embedded Systems Basics Fundamentals of TinyML Machine Learning Basics
- Artificial intelligence Data Science Data Science with 'Python' Machine learning TinyML
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