
Level 2 - AI Specialist
Visual Understanding
Learning method: On campus
Duration: 16 weeks
Major: Artificial Intelligence
ENROLL NOWIntroduction
The Visual Understanding Course for AI Specialist provides top Deep Learning architectures, techniques, models, and algorithms for Visual Understanding, Visual Understanding Environment.
As a result, students are able to build Deep Learning models and AI applications relating to Visual Understanding.
Applicable areas of course

Medical imaging diagnosis

Support for image editing in design

Security/unlock using image processing

Image recognition and quality improvement in photography

Quality improvement of images in films

Image analysis and recognition via camera
Who can learn?

GROUP 1: People who know about python programming, linear algebra, analytics, optimization * People who have logical thinking and critical thinking *

GROUP 2: Students who have been awarded AI Practitioner certificates by VTC Academy
* Subject to the International-standards-based competency assessment of VTC Academy
Course overview
- Excellent Deep Learning architecture, models and algorithms for Visual understanding (computer vision operations, multimodal data, reasoning capacities)
- Various Deep Learning models for Computer vision: Vgg, Inception, u-Net/CheXNet/MrNet, resNet, DenseNet, yoLo MobileNet, shuffleNet, EffNet, etc.
- Deep Learning techniques: generative Adversarial Networks (gANs), Variational Autoencoder (VAE), meta/transfer/few-shot learning, evolutionary search, Bayesopt, DeeprL, etc.
- Topics of Visual Understanding: Image, object, scene, Video, Behavior, 3D, setting, Integration, etc.
- Step-by-step development of Deep Learning models and AI applications
Course outcomes

01
Know how to build and develop different models, algorithms and ideas systematically in Deep Learning for Visual Understanding.
02
Have worked with multimodal data including images, videos, audios, and texts.
03
Can apply reasoning tools in Deep Learning networking for Visual Understanding.
04
Can optimize and adjust Machine Learning models based on the purpose of the implementation (Cloud, Web, Mobile, Edge devices).
05
Can implement new Math solutions in real applications; Design solutions using excellent algorithms; Collect, process, label, train, implement, evaluate, update Machine Learning models.

Testimonials
Participating enterprise
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