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.
Introduction, A conversation with Andrew
Skills You'll Learn
TensorFlow Lite, Mathematical Optimization, Machine Learning, Tensorflow, Object Detection
Reviews
One of the most useful and exciting courses I've ever done! Especially for the information available in the last (4th) week. Very interesting material and full of practical potential!
I am glad I did this course to learn about exciting options to run Tensorflow on a variety of devices. I am thinking about Raspberry Pi and iOS devices in particular
Last week you learned about TensorFlow Lite and you saw how to convert your models from TensorFlow to TensorFlow Lite format. You also learned about the standalone TensorFlow Lite Interpreter which could be used to test these models. You wrapped with an exercise that converted a Fashion MNIST based model to TensorFlow Lite and then tested it with the interpreter.
Taught By
Laurence Moroney
Instructor
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