Computer Vision Training in Brno

This instructor-led, live training in Brno (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 Brno (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 Brno (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:

• Gain a solid understanding of 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 Brno (online or onsite) is designed for beginner-level law enforcement officers who want to transition from manual facial sketching to utilizing AI tools for developing facial recognition systems.

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

• Understand the core principles of Artificial Intelligence and Machine Learning.
• Learn the essentials of digital image processing and its role in facial recognition.
• Develop skills in employing AI tools and frameworks to construct facial recognition models.
• Obtain practical experience in creating, training, and testing facial recognition systems.
• Gain insight into the ethical considerations and best practices for deploying facial recognition technology.

This practical training in Brno focuses on the fundamentals of Fiji and ImageJ for biological research. Participants will develop essential skills in image preprocessing, quantitative analysis, and macro automation, enabling them to optimize workflows for histological tissues, cells, and other samples while ensuring reproducible outcomes.

This instructor-led, live training in Brno (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 Brno (online or onsite) targets intermediate to advanced developers, researchers, and data scientists aiming to learn the implementation of real-time object detection using YOLOv7.

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

• Comprehend the foundational concepts of object detection.
• Install and set up YOLOv7 for object detection tasks.
• Train and validate custom object detection models utilizing YOLOv7.
• Integrate YOLOv7 with various computer vision frameworks and tools.
• Address common issues arising in YOLOv7 implementations.