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Deep Learning Web Service Project 1 - Basics. Object Detect Defective Product Identification

There are many projects that want to provide deep learning programs made with Python as web or mobile services. Learn the process of making Python-based deep learning codes such as TensorFlow, PyTorch, YOLO, OpenCV, and Keras into web service APIs and provide them as mobile web apps, and apply them in practice.

(3.6) 5 reviews

120 learners

Level Intermediate

Course period Unlimited

  • nomad
PWA
PWA
Google App Engine
Google App Engine
Flask
Flask
IONIC
IONIC
PWA
PWA
Google App Engine
Google App Engine
Flask
Flask
IONIC
IONIC

What you will gain after the course

  • Serving Python Deep Learning Code as a Web API

  • Mobile PWA web app, implementing deep learning features on Android devices

  • Object Detection in Images and Videos

  • Computer Vision Image, Video Processing

  • Providing deep learning capabilities as a mobile web service

Turn Python deep learning code into web services for mobile devices!

TensorFlow, PyTorch, YOLO, OpenCV, Keras, etc.
Have you learned deep learning in Python?
Do you want to create a web service regardless of whether it is for mobile or desktop?

We do that in many projects.

There are a growing number of artificial intelligence deep learning projects.
Many people are using mobile web apps for their projects.
You want to start serving right away.
How can I serve code written in Python directly on the web and in apps ?
Create your own deep learning mobile program .

We'll teach you how to create a mobile deep learning program for your project.

📖 Course Introduction

Under the theme of 'Mobile Deep Learning', you will learn step-by-step how to turn an existing Python, YOLO-based Object Detection program into a web app that can be serviced on both mobile and desktop using Python, Flask, Google App Engine, and Ionic.

Artificial intelligence and deep learning are already being applied in many fields. These projects are primarily built using Python code. Furthermore, various Python-based frameworks, such as TensorFlow, PyTorch, YOLO, Keras, and OpenCV, will continue to be used. This lecture will teach you the process and techniques for converting Python code into a web service and serving it in a real-world project.

Whether you're preparing a mobile web app-based deep learning service or working on a project, the techniques and code in this course will serve as valuable guidance. The course is structured to help you create the most essential features for mobile deep learning.

✅ We'll teach you how to create a cloud web service using Google App Engine!

✅ Let's create a process to create Object Detection Python code into a web service.

✅ Implement deep learning services on mobile and desktop with Ionic web apps.

✅ Now create convenient services anywhere with cloud deep learning web services and mobile web apps.

The course is structured so that you can learn fun tasks step by step along with the theory.
After completing the course, you'll be dreaming of your own 'mobile deep learning' project.

🌈 Project Introduction

How do you create a cloud web service?
Learn step-by-step how to create a web service in the cloud using Google App Engine.

How do you serve deep learning Python code in a web app?
You can use Flask to create Python deep learning code as a web service. By following the examples and learning together, you can easily apply it to other deep learning code.

How do I use web services on mobile or desktop?
Let's create a responsive PWA (Progressive Web App) app that can be used on both mobile and desktop using Ionic Hybrid Web App.

Now, let's use YOLO on mobile to detect objects and even find bad apples.
We will apply the YOLO Python program created with deep learning and the function of identifying bad apples using Custom YOLO on mobile devices.

Special lecture

Are you having trouble with deep learning image recognition?

[OpenCV] Python Deep Learning Image Processing Project - Find Son Heung-min!

I recommend this course. For those who find it difficult to take the course separately, we've included a special lecture on YOLO.

I hope this helps.

🙌 What tools do you use?

This course uses Python-based deep learning code that we have previously used.
We used Flask and Google App Engine to serve deep learning code on the web as a new API.
And we used Ionic to create a nice screen with the web app.
In addition to this, I will explain how to install some useful software one by one during the lecture.

🙋🏻‍♂️ I'm curious!

Q. What are the features of this course?
A. We primarily implement deep learning using Python-based programs. In practical projects, many fields want to turn this code into mobile web services. This course not only covers the theoretical aspects of mobile deep learning, but also covers practical projects using Google App Engine and Flask to build deep learning web app services, such as YOLO object detection and defective product identification.

Q. Can non-majors also take the course?
A. Data science isn't a field exclusively for those with a computer science degree. While learning programming languages like Python, Flask, and Ionic is important, understanding digital marketing is equally crucial. With your passion, you can fully learn and apply this knowledge.

Q. Where can I find the project source code and materials?
A. The project source code and related materials used while learning this course can be downloaded for free from the Creapple website ( www.creapple.com ), a knowledge curation portal I run.

Recommended for
these people

Who is this course right for?

  • Anyone who wants to service Python deep learning code on the web

  • Those who want to use deep learning in practice

  • Anyone who wants to implement deep learning on mobile devices

  • Anyone preparing a project related to Computer Vision

  • Anyone who wants example code to use in their Computer Vision projects

Need to know before starting?

  • [OpenCV] Python Deep Learning Image Processing Project - Find Son Heung-min!

  • Ionic, Ionic 100-minute core lecture

  • Willingness to study hard

Hello
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Rating

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Curriculum

All

25 lectures ∙ (3hr 28min)

Course Materials:

Lecture resources
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Reviews

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5 reviews

3.6

5 reviews

  • tfg00749152님의 프로필 이미지
    tfg00749152

    Reviews 1

    Average Rating 5.0

    5

    40% enrolled

    It's really good

    • rifampicin2774님의 프로필 이미지
      rifampicin2774

      Reviews 46

      Average Rating 5.0

      5

      100% enrolled

      It's a good lecture.

      • coojugi0754님의 프로필 이미지
        coojugi0754

        Reviews 1

        Average Rating 3.0

        3

        36% enrolled

        The lecture content is not up-to-date. And the links in the lecture material section are not all connected.

        • hpchoi님의 프로필 이미지
          hpchoi

          Reviews 2

          Average Rating 4.0

          3

          100% enrolled

          Sample code download is not working. Looks like it needs updating and maintenance.

          • jsj1310123510님의 프로필 이미지
            jsj1310123510

            Reviews 1

            Average Rating 2.0

            2

            100% enrolled

            I feel like I paid for the code. There are few parts where the explanation is proper, and at the same time, the instructor asks me to fill in the gaps in his other lectures. If this isn't selling the lecture, then what is? It would be better to raise the price and make the content more substantial. I really don't recommend purchasing the lecture. Think again before paying.

            $42.90

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