DeepLearning.AI - Device-based Models with TensorFlow Lite
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Device-based Models with TensorFlow Lite at Coursera Overview
Duration | 10 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Device-based Models with TensorFlow Lite at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 2 of 4 in the TensorFlow: Data and Deployment Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level Basic understanding of Kotlin and/or Swift
- Approx. 10 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Device-based Models with TensorFlow Lite at Coursera Course details
- 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.
Device-based Models with TensorFlow Lite at Coursera Curriculum
Device-based models with TensorFlow Lite
Introduction, A conversation with Andrew Ng
A few words from Laurence
Features and components of mobile AI
Architecture and performance
Optimization Techniques
Saving, converting, and optimizing a model
Examples
Quantization
TF-Select
Paths in Optimization
Running the models
Transfer learning
Converting a model to TFLite
Transfer learning with TFLite
Prerequisites
Downloading the Coding Examples and Exercises
GPU delegates
Learn about supported ops and TF-Select
Week 1 Wrap up
Exercise Description
Week 1 Quiz
Running a TF model in an Android App
Introduction, A conversation with Andrew
Installation and resources
Architecture of a model
Initializing the Interpreter
Preparing the Input
Inference and results
Code walkthrough
Run the App
Classifying camera images
Initialize and prepare input
Demo of camera image classifier
Initialize model and prepare inputs
Inference and results
Demo of the object detection App
Code for the inference and results
Android fundamentals and installation
Week 2 Wrap up
Description
Week 2 Quiz
Building the TensorFLow model on IOS
Introduction, A conversation with Andrew Ng
A few words from Laurence
What is Swift?
TerserflowLiteSwift
Cats vs Dogs App
Taking the initial steps
Scaling the image
More steps in the process
Looking at the App in Xcode
What have we done so far and how do we continue?
Using the App
App architecture
Model details
Initial steps
Final steps
Looking at the code for the image classification App
Object classification intro
TFL detect App
App architecture
Initial steps
Final steps
Looking at the code for the object detection model
Important links
Apple?s developer's site
Apple's API
More details
Camera related functionalities
The Coco dataset
Week 3 Wrap up
Description
Week 3 Quiz
TensorFlow Lite on devices
Introduction, A conversation with Andrew Ng
A few words from Laurence
Devices
Starting to work on a Raspberry Pi
How do we start?
Image classification
The 4 step process
Object detection
Back to the 4 step process
Raspberry Pi demo
Microcontrollers
Closing words by Laurence
A conversation with Andrew Ng
Edge TPU models
Options to choose from
Pre optimized mobileNet
Object detection model trained on the coco
Suggested links
Description
Wrap up
Week 4 Quiz
Device-based Models with TensorFlow Lite at Coursera Admission Process
Important Dates
Other courses offered by Coursera
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