Računarski vid Training in Srbija

This instructor-led, live training in Srbija (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.

Ova obuka sa trenerom uživo na temu Srbija (online ili na lokaciji) namenjena je stručnjacima naprednog nivoa koji žele da prodube razumevanje računarske vizije i istraže mogućnosti Tenzora u razvoju sofisticiranih vizuelnih modela pomoću Google Colab.

Nakon završetka ove obuke, učesnici će moći da:

• Izgrade i obuku konvolucione neuronske mreže (CNN) koristeći TensorFlow.
• Izveste Google Colab za skalabilni i efikasan razvoj modela u oblaku.
• Implementiraju tehnike preobrade slika za zadatke iz računarske vizije.
• Implementiraju modele računarske vizije za primenu u stvarnom svetu.
• Koriste učenje prenosa (transfer learning) za poboljšavanje performansi CNN modela.
• Vizuelizuju i tumače rezultate modela za klasifikaciju slika.

This instructor-led, live training in Srbija (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.

Ovo instruisano uživo obuke u Srbija (online ili na licu mesta) namenjeno je policijskim službenicima početnog nivoa koji žele da pređu sa ručnog skiciranja lica na korišćenje AI alata za razvojem sistema prepoznavanja lica.

Krajem ove obuke, učesnici će moći da:

• Razumeju osnove veštačke inteligencije i mašinskog učenja.
• Nauče osnove digitalne obrade slika i njihovu primenu u prepoznavanju lica.
• Razviju veštine korišćenja AI alata i okvira za kreiranje modela prepoznavanja lica.
• Steku praktično iskustvo u kreiranju, obuci i testiranju sistema za prepoznavanje lica.
• Razumeju etičke aspekte i najbolje prakse u korišćenju tehnologije prepoznavanja lica.

This practical training in Srbija 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 streamline workflows for histological tissues, cells, and other samples while ensuring reproducible results.

This instructor-led, live training in Srbija (online or onsite) targets intermediate-level professionals aiming to utilize Vision Builder AI for the design, implementation, and optimization of automated inspection systems within SMT (Surface-Mount Technology) processes.

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

• Setting up and configuring automated inspections using Vision Builder AI.
• Acquiring and preprocessing high-quality images for analysis.
• Implementing logic-based decision-making for defect detection and process validation.
• Generating inspection reports and optimizing overall system performance.

This live, instructor-led training in Srbija, offered online or on-site, is designed for intermediate to advanced developers, researchers, and data scientists aiming to implement real-time object detection using YOLOv7.

By the conclusion of this program, participants will be capable of:

• Understanding the foundational concepts of object detection.
• Installing and configuring YOLOv7 for detection tasks.
• Training and testing custom object detection models using YOLOv7.
• Integrating YOLOv7 with diverse computer vision frameworks and tools.
• Diagnosing and fixing common issues in YOLOv7 implementations.