Learning TinyML: A Hands-On Course

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Archana Vaidheeswaran

1:10:50

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01 - Introduction

01 - Getting started with TinyML.mp4

00:53

02 - What is TinyML.mp4

04:23

03 - What you should know.mp4

01:31

02 - 1. Is Your Problem a TinyML Problem

01 - Defining constraints.mp4

02:20

02 - Checklist for a TinyML problem.mp4

04:49

03 - 2. Solving the Constraints Optimization Techniques

01 - Pre-trained models.mp4

04:33

02 - Quantization and types of quantization.mp4

04:17

03 - TFLite post training quantization.mp4

05:09

04 - Quantization awareness training in TFLite.mp4

03:41

05 - Pruning.mp4

04:32

06 - Knowledge distillation.mp4

03:33

07 - Challenge Compare results of optimization.mp4

03:30

08 - Solution Compare results of optimization.mp4

03:53

04 - 3. Deploying TinyML Models

01 - Edge impulse.mp4

02:17

02 - Deploy a classification project to your phone.mp4

02:51

03 - Challenge Deploy a regression model to your phone.mp4

01:03

04 - Solution Deploy a regression model to your phone.mp4

02:14

05 - 4. TinyMLOps

01 - Hardware devices.mp4

04:47

02 - Bringing all the concepts together.mp4

04:00

06 - Conclusion

01 - Resources for TinyML.mp4

02:54

02 - Future of the TinyML Research directions.mp4

03:02

03 - Next steps with TinyML.mp4

00:38

Description

While you may not realize it, TinyML probably affects your life in some way on a daily basis. If you have a smartphone or IoT device that features voice activation, facial recognition, audio detection, or other functions that employ machine learning algorithms, you have TinyML to thank. In this course, instructor Archana Vaidheeswaran guides you into the world of TinyML and shows you how you can process huge AI models right in the palm of your hand. Archana starts by teaching you how to identify if your ML/AI problem is a TinyML problem, then shows you optimization techniques to fit your deep learning models and illustrates multiple use cases. She explains quantization techniques, how to train a model using Tflite, how to deploy a TinyML model, and covers the entire TinyMLOps lifecycle. Archana finishes the course with a look at what’s in store for the future of TinyML, along with some resources you can use to continue your learning.

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Machine Learning

Archana Vaidheeswaran

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I am here to learn,grow and help other people get motivated with my ideas. I wish to get acquainted with as many professionals as possible.

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