Online Computer Vision Training in Taiwan

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

Upon completing this training, participants will be able to:

• Grasp 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.
• Utilize frameworks such as 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 Taiwan (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:

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

This instructor-led, live training in Taiwan (online or onsite) is aimed at 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:

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

This instructor-led, live training in Taiwan (online or onsite) is designed for beginner-level law enforcement personnel seeking to transition from manual facial sketching to utilizing AI tools for building facial recognition systems.

Upon completion of this training, participants will be able to:

• Grasp the fundamental concepts of Artificial Intelligence and Machine Learning.
• Learn the core principles of digital image processing and their application in facial recognition.
• Acquire skills in using AI tools and frameworks to construct facial recognition models.
• Gain practical experience in creating, training, and testing facial recognition systems.
• Understand the ethical considerations and best practices associated with the use of facial recognition technology.

This practical training module on Taiwan delves into the fundamentals of Fiji and ImageJ for biological research. Participants will acquire hands-on competencies in image preprocessing, quantitative evaluation, and macro automation, enabling the optimization of workflows for histological tissues, cells, and diverse samples while ensuring reproducible results.

This instructor-led, live training in Taiwan (online or onsite) targets 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 instructor-led, live training in Taiwan (online or onsite) is aimed at intermediate to advanced-level developers, researchers, and data scientists who wish to learn how to implement real-time object detection 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.