Computer Vision Training in Ankara

This instructor-led, live training in Ankara (online or onsite) is designed for intermediate to advanced computer vision engineers, AI developers, and IoT professionals 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 Ankara (available online or onsite) is designed for advanced professionals aiming to expand their expertise in computer vision and investigate TensorFlow's potential for creating sophisticated vision models via Google Colab.

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

• Construct 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 Ankara (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 Ankara (online or onsite) is designed for beginner-level law enforcement personnel aiming 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 fundamental principles of Artificial Intelligence and Machine Learning.
• Learn the essentials of digital image processing and its application in facial recognition.
• Develop proficiency in using AI tools and frameworks to construct facial recognition models.
• Gain practical experience in creating, training, and testing facial recognition systems.
• Understand ethical considerations and best practices associated with facial recognition technology.

This hands-on training in Ankara delves into the fundamentals of Fiji and ImageJ for biological research. Participants will develop practical proficiency 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 Ankara (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 live, instructor-led training in Ankara, offered online or on-site, is designed for intermediate to advanced developers, researchers, and data scientists seeking to implement real-time object detection using YOLOv7.

By the end of the course, participants will be able to:

• Comprehend the foundational concepts of object detection.
• Install and set up YOLOv7 for detection workflows.
• Train and test custom detection models with YOLOv7.
• Combine YOLOv7 with other computer vision frameworks and utilities.
• Debug common issues arising from YOLOv7 implementation.