Computer Vision Training in Bhutan

This instructor-led, live training in Bhutan (online or onsite) is aimed at intermediate to advanced 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 Bhutan (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) delivers robust deployment and optimization capabilities for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.

This instructor-led, live training (available online or onsite) targets intermediate-level AI professionals seeking to build, deploy, and optimize vision and language models using the CANN SDK for production-grade solutions.

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

• Deploy and optimize CV and NLP models leveraging CANN and AscendCL.
• Utilize CANN utilities to convert models and seamlessly integrate them into active pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines suitable for edge or cloud-based deployment environments.

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on labs focusing on model deployment and performance profiling.
• Live pipeline design exercises utilizing real-world CV and NLP use cases.

Customization Options

• To request a customized training session for this course, please contact us to make arrangements.

This instructor-led, live training in Bhutan (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:

• Grasp 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 Bhutan (offered online or on-site) is targeted at beginner-level law enforcement staff seeking to transition from manual facial sketching to utilizing AI tools for developing facial recognition systems.

By the conclusion 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 instructor-led, live training in Bhutan (online or onsite) is designed for beginner to intermediate-level researchers and laboratory professionals who need to process and analyze images of histological tissues, blood cells, algae, and other biological specimens.

Upon completing this training, participants will be able to:

• Navigate the Fiji interface and effectively use ImageJ’s core capabilities.
• Preprocess and enhance scientific images to improve analytical accuracy.
• Perform quantitative image analysis, including cell counting and area measurements.
• Automate routine tasks using macros and plugins.
• Tailor workflows to meet specific image analysis requirements in biological studies.

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

Upon completing this training, participants will be equipped to:

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

This instructor-led, live training in Bhutan (online or on-site) is designed for intermediate to advanced developers, researchers, and data scientists keen on implementing 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.

Computer Vision is a domain focused on the automatic extraction, analysis, and comprehension of valuable insights from digital media. Python is a high-level programming language renowned for its clean syntax and code readability.

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

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

• Grasp the fundamentals of Computer Vision
• Utilize Python to execute Computer Vision tasks
• Develop their own systems for face, object, and motion detection

Audience

• Python programmers interested in Computer Vision

Course Format

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