Computer Vision Training in Caruaru

This instructor-led, live training in Caruaru (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 Caruaru (online or onsite) targets advanced professionals seeking 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 Caruaru (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.
• Develop 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 guided, live training in Caruaru (online or on-site) targets entry-level law enforcement officers seeking to transition from manual facial sketching to employing AI tools for developing facial recognition systems.

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

• Grasp the core principles of Artificial Intelligence and Machine Learning.
• Master the fundamentals of digital image processing and its role in facial recognition.
• Acquire proficiency in utilizing AI tools and frameworks to construct facial recognition models.
• Obtain practical experience in designing, training, and evaluating facial recognition systems.
• Comprehend the ethical implications and established best practices surrounding facial recognition technology.

This practical training module in Caruaru provides a comprehensive look at Fiji and ImageJ fundamentals 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 various other samples while ensuring reproducible results.

This instructor-led, live training in Caruaru (online or onsite) is aimed at 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 course in Caruaru (delivered 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 training, participants will be able to:

• Comprehend the fundamental concepts of object detection.
• Install and set up YOLOv7 for object detection workflows.
• Develop and test custom object detection models with YOLOv7.
• Connect YOLOv7 with additional computer vision frameworks and utilities.
• Address typical issues arising during YOLOv7 implementation.