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Deep Learning: masked face detection, recognition

Deep Learning: masked face detection, recognition
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 50 lectures (10h 50m) | Size: 5.13 GB

SSD face and facial mask detection, MTCNN face detection, and train your own model to recognize faces even with masks
What you'll learn:
How to install Python, Tensorflow, Pycharm from scratch
How to create your own classification model
What's FaceNet
What's the difference between classification models and face recognition models
How to create your own FaceNet model by modifying the classification model
How to do the face alignment using SSD face detection
How to do the face alignment using MTCNN face detection
How to do the data cleaning
How to create masked face dataset
How to train your FaceNet model
What are training skills
How to implement training skills to train models effectively
How to perform the real time face detection, mask detection, and face recognition
High school mathematics level
Basic Python and Tensorflow
Desktop or laptop with at least 6GB Nvidia GPU cards
A USB camera
Deep Learning of artificial intelligence(AI) is an exciting future technology with explosive growth.
Masked face recognition is a mesmerizing topic which contains several AI technologies including classifications, SSD object detection, MTCNN, FaceNet, data preparation, data cleaning, data augmentation, training skills, etc.
Nowadays, people are required to wear masks due to the COVID-19 pandemic.
The conventional FaceNet model barely recognizes faces without masks
Even the FaceID on iPhone or iPad devices only works without masks.
In this course, I will teach you how to train a model that works with masks.
In the final presentation, you will be able to very proudly perform the real time face detection, face mask detection, and face recognition, even with masks!
Windows is the operating system so you will not need to learn Linux first.
I will instruct you to install Python, Tensorflow, and Pycharm IDE from scratch.
Having basic Python and Tensorflow knowledge will be an advantage, but if not, I will have a quick guide to both for you.
In my tutorials, I would like to explain difficult theories and formulas by easy concepts or practical examples.
Model training always takes a lot of time.
Take this project as an example, it needs more than 400,000 images to train.
I've spent much time on training skills.
After numerous experimental confirmation, I summarized my methods by Descriptions to let you digest easily.
These training skills can be not only applied in face recognition but also in other models.
All lectures are spoken in plain English.
If you feel my speaking pace is quite slow, you can use the gear setting to speed up.
The auto-generated captions may have errors. I will review and correct them.
Achievement from the topic, skills grow from the project. I hope you enjoy the fun of AI.
Who this course is for
Anyone interested in Deep Learning or Face Recognition
Any students in university who tend to start a project in Deep Learning
Any engineers who want to level up in Deep Learning
Deep Learning: masked face detection, recognition

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