Harvard

Vijay Janapa Reddi

Laurence Moroney

In this course, you will delve into the practical applications of TinyML, guided by industry leaders. Learn about the code behind widely-used TinyML devices and explore real-world case studies. The course covers key topics such as Keyword Spotting, Visual Wake Words, Anomaly Detection, and Responsible AI Development, providing you with the skills to deploy TinyML solutions effectively.

Applications of TinyML

Overview of TinyML and its significance in the field of machine learning.

Introduction to TinyML

Understanding the principles and applications of keyword spotting in TinyML.

Keyword Spotting

Exploring the concept of visual wake words and their implementation in TinyML.

Visual Wake Words

Learning about anomaly detection techniques and their use in TinyML applications.

Anomaly Detection

Principles of dataset engineering for effective TinyML model training.

Dataset Engineering

Guidelines and best practices for developing responsible AI solutions using TinyML.

Responsible AI Development

Examination of real-world TinyML applications and deployment challenges.

Real-World Case Studies

Focus on training and inference of neural networks in TinyML applications.

Neural Network Training and Inference

Using sensor data for tasks such as gesture detection and voice recognition.

Sensor Data Utilization

Explore the fascinating world of TinyML and its real-world applications. This course offers insights into the code behind popular TinyML devices, focusing on neural networks and sensor data utilization. Enroll now to enhance your understanding of this rapidly growing field.

Individuals interested in understanding the applications of TinyML in real-world scenarios.

Tech Enthusiasts

Professionals looking to integrate TinyML solutions into their existing systems.

Industry Professionals

Students pursuing studies in machine learning and artificial intelligence.

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Applications of TinyML

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