Overview What You Will Learn Curriculum Instructor

Course Overview

Deep Learning of artificial intelligence (AI) is an exciting future technology with explosive growth. Nowadays, people are required to wear masks due to the COVID-19 pandemic. The conventional FaceNet model barely recognizes faces without masks. Even FaceID on iPhone or iPad devices only works without masks.

In this course, we will learn how to train a model that works with masks. Masked face recognition is a mesmerizing topic that contains several AI technologies including classifications, SSD object detection, MTCNN, FaceNet, data preparation, data cleaning, data augmentation, training skills, etc. You will be training 400,000 images during the entire journey of the course. These training skills can be not only applied in face recognition but also in other models.

By the end of the course, you will be able to very proudly perform real-time face detection, face mask detection, and face recognition, even with masks!

What You Will Learn

  • How to create your own FaceNet model by modifying the classification model
  • How to do the face alignment using MTCNN face detection
  • How to create masked face dataset
  • How to perform the real time face detection
  • mask detection
  • and face recognition
  • How to train your FaceNet model
  • How to implement training skills to train models effectively

Program Curriculum

  • Environment Installation
  • $3.5 Million Cybersecurity Scholarship by EC-Council

  • Jupyter Notebook
  • How to Use Jupyter Notebook
  • Chapter 2 Quiz

  • Lecture_1
  • Lecture_2
  • Lecture_3
  • How to Distribute the Dataset into Train and Test Set
  • Chapter 3 Quiz

  • What is a Classification Model?
  • Recall, Precision, and Accuracy
  • Elements of a Classification Model
  • Chapter 4 Quiz

  • Introduction_1
  • Introduction_2
  • Introduction_3
  • Introduction_4
  • Introduction_5
  • Let's Write Codes
  • Chapter 5 Quiz

  • Overview of Classification Model
  • Class Initialization
  • Model Initialization
  • Model Train Method
  • Save CKPT, PB Files, and Log Files
  • Chapter 6 Quiz

  • Concepts_1
  • Concepts_2
  • Chapter 7 Quiz

  • An Introduction of Inception ResNet V1
  • Create FaceNet Model by Modifying the Classification Model
  • Chapter 8 Quiz

  • Concepts
  • Introduction of SSD Face Detection Method
  • Write the Program
  • Chapter 9 Quiz

  • Concepts and Write Codes
  • Chapter 10 Quiz

  • Concepts
  • Cleaning Function Introduction
  • Cleaning Function Explanation_1
  • Cleaning Function Explanation_2
  • Cleaning Function Explanation_3
  • Cleaning Function Explanation_4
  • Cleaning Function Explanation_5
  • Cleaning Function Explanation_6
  • Chapter 11 Quiz

  • Explanation of This Method
  • Let's Write Codes
  • Test the Program
  • Chapter 12 Quiz

  • Modify the Codes of Inputs
  • Real Training is Started
  • Modify the Codes of "check results"
  • Chapter 13 Quiz

  • Concepts and Code Explanation
  • Modify the Codes of "get_4D_data"
  • Chapter 14 Quiz

  • Concepts, Write Codes and Get the Accuracy
  • Chapter 15 Quiz

  • Concepts and Code Explanation
  • Modify the Class
  • Results and Discussion
  • Chapter 16 Quiz

  • Explanation_1
  • Explanation_2
  • Let's Write Codes
  • Use a Laptop to Perform the Real Time Face Recognition
  • Chapter 17 Quiz

Master and Apprentice Method

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Instructor

Yuan Po Liao

Johnny has an MS in electrical engineering from National Central University in Taiwan. He used to be a cellphone hardware engineer in the Hon Hai Technology Group. For the interest of coding, he learned C and Python by self-study. He completed IOT Wifi and Zigbee systems applied in TSMC, Arcadyan, Delta, Mitac, and ITRI. With the explosive growth of AI, he learned AI basics by self-study. He has been a full-time AI computer vision engineer since he left his previous firmware system engineer job several years ago.

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