Computer Vision Training in Uzbekistan

This instructor-led, live training in Uzbekistan (online or on-site) is designed for intermediate to advanced-level computer vision engineers, AI developers, and IoT professionals who aim to implement and optimise 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 optimised deep learning models on edge devices for real-time image and video analysis.
• Utilise frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimise AI models for performance, power efficiency, and low-latency inference.

This instructor-led, live training in Uzbekistan (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 powerful deployment and optimisation tools for real-time AI applications in computer vision and natural language processing (NLP), particularly on Huawei Ascend hardware.

This instructor-led, live training (available online or on-site) is designed for intermediate-level AI practitioners who aim to build, deploy, and optimise vision and language models using the CANN SDK for production-grade use cases.

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

• Deploy and optimise computer vision (CV) and NLP models using CANN and AscendCL.
• Leverage CANN tools to convert models and integrate them into live pipelines.
• Optimise inference performance for tasks such as object detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment scenarios.

Course Format

• Interactive lectures and demonstrations.
• Practical labs covering model deployment and performance profiling.
• Live pipeline design using real-world CV and NLP use cases.

Course Customisation Options

• To request a customised version of this course, please contact us to arrange the details.

This guided, live training in Uzbekistan (online or in-person) targets intermediate-level AI developers and computer vision engineers who aim to build robust vision systems for autonomous driving applications.

Upon completion of this training, participants will be capable of:

• Grasping the core principles of computer vision within autonomous vehicles.
• Developing algorithms for object detection, lane recognition, and semantic segmentation.
• Combining vision systems with other autonomous vehicle components.
• Utilizing deep learning methods for sophisticated perception tasks.
• Assessing the performance of computer vision models in practical situations.

This instructor-led, live training in Uzbekistan (online or onsite) targets beginner-level law enforcement personnel who aim to shift from manual facial sketching to leveraging AI tools for facial recognition system development.

Upon completion of this training, participants will be capable of:

• Grasping the core concepts of Artificial Intelligence and Machine Learning.
• Mastering the fundamentals of digital image processing and its role in facial recognition.
• Acquiring practical skills in employing AI tools and frameworks to construct facial recognition models.
• Gaining hands-on experience in the creation, training, and testing of facial recognition systems.
• Understanding ethical implications and industry best practices regarding facial recognition technology.

This instructor-led, live training in Uzbekistan (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 Uzbekistan (available online or on-site) is designed for intermediate-level professionals who aim to use Vision Builder AI to design, implement, and optimise automated inspection systems for SMT (Surface-Mount Technology) processes.

Upon completion 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 detailed analysis.
• Implement logic-based decision-making for defect detection and process validation.
• Generate comprehensive inspection reports and optimise system performance.

This instructor-led, live training in Uzbekistan (available online or on-site) is tailored for intermediate to advanced-level developers, researchers, and data scientists who aim to master the implementation of real-time object detection using YOLOv7.

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

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

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

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

Upon completing this training, participants will be equipped to:

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

Audience

• Python developers seeking to delve into Computer Vision

Course Format

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