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Device-based Models with TensorFlow Lite

Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various...

By Laurence Moroney on Coursera

About This Course

Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. This second course teaches you how to run your machine learning models in mobile applications. You’ll learn how to prepare models for a lower-powered, battery-operated devices, then execute models on both Android and iOS platforms. Finally, you’ll explore how to deploy on embedded systems using TensorFlow on Raspberry Pi and microcontrollers. This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

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Frequently Asked Questions

How much does Device-based Models with TensorFlow Lite cost?

Visit the Device-based Models with TensorFlow Lite course page for current pricing and available discounts.

Who teaches Device-based Models with TensorFlow Lite?

Device-based Models with TensorFlow Lite is taught by Laurence Moroney, DeepLearning.AI.

What skill level is Device-based Models with TensorFlow Lite for?

This course is designed for advanced learners.

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Students0
Duration5 hours
LevelAdvanced
Languageen
PlatformCoursera