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

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

📖 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.

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.