LinkedIn – Learning TinyML: A Hands-On Course 2022-7

Learning TinyML: A Hands-On Course. This course introduces you to the exciting world of TinyML and shows you how to run large AI models in the palm of your hand. Whether you’re a beginner or an expert, this course is for anyone who wants to gain a deep understanding of TinyML. In this course, you’ll learn how to transform a problem into a TinyML problem, optimize your deep learning models, and deploy them on low-power devices. You’ll also learn key concepts of TinyMLOps, and finally, get a look at the future of the field and resources available for further learning.

TinyML allows us to harness the power of AI on small, low-power devices. This technology is already used in many of our everyday devices, including smartphones, smartwatches, and smart speakers. In this course, you will learn how to optimize deep learning models to run on these devices using various tools and techniques. You will also learn about the challenges involved in developing TinyML systems and how to overcome them.

What you will learn:

• Defining a Problem as a TinyML Problem: Learn how to identify problems that can be solved using TinyML.
• Optimization techniques: Reducing the size and complexity of deep learning models to run on resource-constrained devices.
• Quantization: Converting high-precision numbers to lower-precision numbers to reduce the size of the model.
• Model Training Using TensorFlow Lite: Training Deep Learning Models for Mobile Devices and IoT.
• Deploying TinyML models: Placing trained models on different devices.
• TinyMLOps: Manage the lifecycle of TinyML models from training to deployment.
• The Future of TinyML: A review of research trends and future applications of TinyML.

This course is suitable for people who:

• Are interested in machine learning and artificial intelligence.
• They want to learn about practical applications of machine learning on small devices.
• They are software developers, hardware engineers, and students of related fields.
• Want to develop their own projects using TinyML.