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Device-based Models with TensorFlow Lite
  • Level Professional
  • المدة 11 ساعات hours
  • الطبع بواسطة DeepLearning.AI
  • Offered by

عن

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.

الوحدات

Course Introduction

    • 1 Videos
    • 2 Readings
Show info about module content

1 Videos

  • Introduction, A conversation with Andrew Ng

2 Readings

  • Prerequisites
  • Downloading the Ungraded Labs and Programming Assignments

Machine Learning Models in Mobile and Embedded Systems

    • 4 Videos
    • 2 Readings
Show info about module content

4 Videos

  • A few words from Laurence
  • Features and components of mobile AI
  • Architecture and performance
  • Optimization Techniques

2 Readings

  • GPU delegates
  • [IMPORTANT] Have questions, issues or ideas? Join our Forum!

Taking a look at the saved model format

    • 5 Videos
    • 1 Readings
Show info about module content

5 Videos

  • Saving, converting, and optimizing a model
  • Examples
  • Quantization
  • TF-Select
  • Paths in Optimization

1 Readings

  • Learn about supported ops and TF-Select

First primer on running models on mobile devices

    • 4 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Week 1 Quiz

4 Videos

  • Running the models
  • Transfer learning
  • Converting a model to TFLite
  • Transfer learning with TFLite

1 Readings

  • Week 1 Wrap up

Lecture Notes (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Lecture Notes Week 1

Graded Exercise - Train Your Own Model and Convert It to TFLite

    • 1 Readings
    • 1 Lab
    • 1 Programming
Show info about module content

1 Programming

  • Exercise 1 - Train Your Own Model and Convert It to TFLite

1 Labs

  • Exercise 1 - Train Your Own Model and Convert It to TFLite

1 Readings

  • Exercise Description

Introduction

    • 6 Videos
    • 1 Readings
Show info about module content

6 Videos

  • Introduction, A conversation with Andrew
  • Installation and resources
  • Architecture of a model
  • Initializing the Interpreter
  • Preparing the Input
  • Inference and results

1 Readings

  • Android fundamentals and installation

Basic image classification

    • 2 Videos
Show info about module content

2 Videos

  • Code walkthrough
  • Run the App

Classifying camera images

    • 2 Videos
Show info about module content

2 Videos

  • Classifying camera images
  • Initialize and prepare input

Code walkthrough - camera image classifier

    • 1 Videos
Show info about module content

1 Videos

  • Demo of camera image classifier

Object detection

    • 2 Videos
Show info about module content

2 Videos

  • Initialize model and prepare inputs
  • Inference and results

Code walkthrough of an object detection app

    • 2 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Week 2 Quiz

2 Videos

  • Demo of the object detection App
  • Code for the inference and results

1 Readings

  • Week 2 Wrap up

Lecture Notes (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Lecture Notes Week 2

Optional Exercise: Rock Paper Scissors for Android

    • 1 Readings
Show info about module content

1 Readings

  • Description

Introduction

    • 8 Videos
    • 3 Readings
Show info about module content

8 Videos

  • Introduction, A conversation with Andrew Ng
  • A few words from Laurence
  • What is Swift?
  • TensorFlowLiteSwift
  • Cats vs Dogs App
  • Taking the initial steps
  • Scaling the image
  • More steps in the process

3 Readings

  • Important links
  • Apple’s developer's site 
  • Apple's API

Next steps

    • 7 Videos
    • 2 Readings
Show info about module content

7 Videos

  • 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

2 Readings

  • More details
  • Camera related functionalities

Classification and detection

    • 7 Videos
    • 2 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Week 3 Quiz

7 Videos

  • 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

2 Readings

  • The Coco dataset
  • Week 3 Wrap up

Lecture Notes (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Lecture Notes Week 3

Optional Exercise - Rock, Paper, Scissors on iOS

    • 1 Readings
Show info about module content

1 Readings

  • Description

Introduction

    • 3 Videos
    • 1 Readings
Show info about module content

3 Videos

  • Introduction, A conversation with Andrew Ng
  • A few words from Laurence
  • Devices

1 Readings

  • Edge TPU models

Example: Raspberry Pi

    • 6 Videos
    • 3 Readings
Show info about module content

6 Videos

  • 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

3 Readings

  • Options to choose from
  • Pre optimized mobileNet
  • Object detection model trained on the coco

Raspberry pi demo

    • 1 Videos
Show info about module content

1 Videos

  • Raspberry Pi demo

Microcontrollers

    • 2 Videos
    • 2 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Week 4 Quiz

2 Videos

  • Microcontrollers
  • Closing words by Laurence

2 Readings

  • Suggested links
  • [IMPORTANT] Reminder about end of access to Lab Notebooks

Lecture Notes (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Lecture Notes Week 4

Optional Exercise - Rock Paper Scissors on Raspberry Pi

    • 1 Readings
Show info about module content

1 Readings

  • Description

Course 2 Wrap up

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • A conversation with Andrew Ng

1 Readings

  • Wrap up

Auto Summary

The "Device-based Models with TensorFlow Lite" course, offered by Coursera, focuses on running machine learning models on mobile applications and embedded systems. Taught by industry experts, it covers model preparation for low-power devices, execution on Android and iOS, and deployment on Raspberry Pi and microcontrollers. Suitable for IT and Computer Science professionals, the course spans 660 minutes and is available through Starter, Professional, and Paid subscriptions. Ideal for learners with a background in TensorFlow, it builds on the TensorFlow in Practice Specialization.

Laurence Moroney
Instructor

Laurence Moroney