Computer Vision Training in Vietnam

This instructor-led, live training in Vietnam (online or onsite) is aimed at intermediate-level to advanced-level computer vision engineers, AI developers, and IoT professionals who wish to implement and optimize computer vision models for real-time processing on edge devices.

By the end of this training, participants will be able to:

• Understand the fundamentals of Edge AI and its applications in computer vision.
• Deploy optimized deep learning models on edge devices for real-time image and video analysis.
• Use frameworks like TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimize AI models for performance, power efficiency, and low-latency inference.

This instructor-led, live training in Vietnam (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

By the end of this training, participants will be able to:

• Construct and train convolutional neural networks (CNNs) utilizing TensorFlow.
• Utilize Google Colab for scalable and efficient cloud-based model development.
• Apply image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Employ transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led, live training in Vietnam (online or onsite) targets intermediate-level AI developers and computer vision engineers who wish to build robust vision systems for autonomous driving applications.

By the end of this training, participants will be able to:

• Grasp the fundamental concepts of computer vision within autonomous vehicles.
• Implement algorithms for object detection, lane detection, and semantic segmentation.
• Integrate vision systems with other subsystems of autonomous vehicles.
• Utilize deep learning techniques for advanced perception tasks.
• Evaluate the performance of computer vision models in real-world scenarios.

This instructor-led, live training in Vietnam (online or on-site) targets beginner-level law enforcement officers who wish to transition from manual facial sketching to using AI tools for developing facial recognition systems.

By the end of this training, participants will be able to:

• Understand the fundamentals of Artificial Intelligence and Machine Learning.
• Learn the basics of digital image processing and its application in facial recognition.
• Develop skills in using AI tools and frameworks to create facial recognition models.
• Gain hands-on experience in creating, training, and testing facial recognition systems.
• Understand ethical considerations and best practices in the use of facial recognition technology.

This hands-on training in Vietnam focuses on the fundamentals of Fiji and ImageJ for biological research. Learners will develop practical skills in image preprocessing, quantitative analysis, and macro automation, enabling them to streamline workflows for histological tissues, cells, and other samples while ensuring reproducible results.

This instructor-led live training in Vietnam (online or onsite) is aimed at intermediate-level professionals who wish to use Vision Builder AI to design, implement, and optimize automated inspection systems for SMT (Surface-Mount Technology) processes.

By the end of this training, participants will be able to:

• Set up and configure automated inspections using Vision Builder AI.
• Acquire and preprocess high-quality images for analysis.
• Implement logic-based decisions for defect detection and process validation.
• Generate inspection reports and optimize system performance.

This live, instructor-led training in Vietnam, offered online or onsite, is designed for intermediate to advanced developers, researchers, and data scientists aiming to learn real-time object detection implementation using YOLOv7.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of object detection.
• Install and configure YOLOv7 for object detection tasks.
• Train and test custom object detection models using YOLOv7.
• Integrate YOLOv7 with other computer vision frameworks and tools.
• Troubleshoot common issues related to YOLOv7 implementation.