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 course is part of the TensorFlow: Data and Deployment Specialization
Offered By


About this Course
Basic understanding of Kotlin and/or Swift
What you will learn
Prepare models for battery-operated devices
Execute models on Android and iOS platforms
Deploy models on embedded systems like Raspberry Pi and microcontrollers
Skills you will gain
- TensorFlow Lite
- Mathematical Optimization
- Machine Learning
- Tensorflow
- Object Detection
Basic understanding of Kotlin and/or Swift
Offered by
Syllabus - What you will learn from this course
Device-based models with TensorFlow Lite
Running a TF model in an Android App
Building the TensorFLow model on IOS
TensorFlow Lite on devices
Reviews
- 5 stars77.09%
- 4 stars16.60%
- 3 stars4.72%
- 2 stars0.87%
- 1 star0.69%
TOP REVIEWS FROM DEVICE-BASED MODELS WITH TENSORFLOW LITE
The material is really interesting. The ability to try out trained models on your own device is awesome! However there are some errors in tasks, Week 4 seems a little bit raw
Quite good course. It gives an opportunity for individuals to utilize tensor flow in day to day devices which makes it more appealing. Thanks for developing this course.
Very advanced content at this time of Deep Learning Era.
Many Thanks for Mr.Laurence Moroney for his special efforts.
Good introduction into getting TensorFlow models up and running on different platforms from microcontrollers, raspberry PI through to IOS and Android
About the TensorFlow: Data and Deployment Specialization

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