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.

The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization tools for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.

This instructor-led, live training (available online or onsite) is designed for intermediate-level AI practitioners looking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.

Upon completing this training, participants will be able to:

• Deploy and optimize CV and NLP models using CANN and AscendCL.
• Utilize CANN tools to convert models and integrate them into live pipelines.
• Optimize inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines suitable for edge or cloud-based deployment scenarios.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab involving model deployment and performance profiling.
• Live pipeline design utilizing real-world CV and NLP use cases.

Course Customization Options

• To request customized training for this course, please contact us to arrange.

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 instructor-led, live training in Taiwan (online or onsite) is aimed at beginner-level to intermediate-level researchers and laboratory professionals who wish to process and analyze images related to histological tissues, blood cells, algae, and other biological samples.

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

• Navigate the Fiji interface and utilize ImageJ’s core functions.
• Preprocess and enhance scientific images for better analysis.
• Analyze images quantitatively, including cell counting and area measurement.
• Automate repetitive tasks using macros and plugins.
• Customize workflows for specific image analysis needs in biological research.

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 tailored for intermediate to advanced developers, researchers, and data scientists eager to master the implementation of real-time object detection using YOLOv7.

Upon completing this training, participants will be equipped to:

• Grasp the core concepts of object detection.
• Install and configure YOLOv7 for object detection tasks.
• Train and evaluate custom object detection models utilizing YOLOv7.
• Seamlessly integrate YOLOv7 with other computer vision frameworks and tools.
• Troubleshoot common issues arising from YOLOv7 implementation.

Fiji is a powerful open-source image processing package that bundles ImageJ (a program designed for scientific multidimensional images) along with a comprehensive suite of plugins for scientific image analysis.

In this instructor-led, live training, participants will learn how to leverage the Fiji distribution and its underlying ImageJ program to create robust image analysis applications.

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

• Use Fiji's advanced programming features and software components to extend ImageJ capabilities
• Stitch large 3D images from overlapping tiles
• Automate the update of a Fiji installation on startup using the integrated update system
• Select from a broad selection of scripting languages to build custom image analysis solutions
• Utilize Fiji's powerful libraries, such as ImgLib, to process large bioimage datasets efficiently
• Deploy applications and collaborate effectively with other scientists on similar projects

Format of the Course

• Interactive lecture and discussion
• Extensive exercises and practical application
• Hands-on implementation in a live-lab environment

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Taiwan (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.

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

• View, load, and classify images and videos using OpenCV 4.
• Implement deep learning in OpenCV 4 with TensorFlow and Keras.
• Run deep learning models and generate impactful reports from images and videos.

Computer Vision is a discipline dedicated to automatically extracting, analyzing, and interpreting valuable information from digital media. Python, a high-level programming language, is renowned for its clear syntax and high code readability.

Through this instructor-led live training, participants will master the fundamentals of Computer Vision by building a series of simple Computer Vision applications using Python.

Upon completing this training, participants will be able to:

• Grasp the core concepts of Computer Vision
• Leverage Python to execute Computer Vision tasks
• Develop custom systems for face, object, and motion detection

Audience

• Python programmers seeking to specialize in Computer Vision

Course Format

• A blend of lectures, discussions, exercises, and extensive hands-on practice

SimpleCV is an open-source framework, which means it is a collection of libraries and software that you can use to develop vision applications. It lets you work with the images or video streams that come from webcams, Kinects, FireWire and IP cameras, or mobile phones. It helps you build software to make your various technologies not only see the world, but understand it too.

Audience

This course is directed at engineers and developers seeking to develop computer vision applications with SimpleCV.

This instructor-led, live training in Taiwan (online or onsite) targets backend developers and data scientists who wish to incorporate pre-trained YOLO models into their enterprise-driven programs and implement cost-effective components for object detection.

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

• Install and configure the necessary tools and libraries required for object detection using YOLO.
• Customize Python command-line applications that operate based on YOLO pre-trained models.
• Deploy the pre-trained YOLO model framework for various computer vision projects.
• Convert existing datasets for object detection into the YOLO format.
• Understand the core concepts of the YOLO algorithm for computer vision and/or deep learning.