Computer Vision Training in Stockholm

This instructor-led, live training session in Stockholm (online or onsite) targets computer vision engineers, AI developers, and IoT professionals at an intermediate to advanced level who aim 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 core principles of Edge AI and its practical applications in computer vision.
• Deploy optimized deep learning models on edge devices to facilitate real-time image and video analysis.
• Utilize frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for deploying models.
• Optimize AI models to enhance performance, power efficiency, and achieve low-latency inference.

This instructor-led, live training in Stockholm (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) delivers robust deployment and optimization capabilities for real-time AI applications in computer vision and natural language processing, particularly when leveraging Huawei Ascend hardware.

This instructor-led training, available online or onsite, is designed for intermediate-level AI professionals seeking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.

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

• Deploy and optimize CV and NLP models using CANN and AscendCL.
• Utilize CANN tools to convert models and integrate them into live pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines suitable for both edge and cloud deployment scenarios.

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on labs focusing on model deployment and performance profiling.
• Live pipeline design exercises using real-world CV and NLP use cases.

Course Customization Options

• For customized training arrangements, please contact us to discuss your specific needs.

This instructor-led, live training in Stockholm (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 Stockholm (online or onsite) is designed for beginner-level law enforcement personnel who wish to transition from manual facial sketching to using AI tools for developing facial recognition systems.

Upon completing this training, participants will be able to:

• Grasp the fundamentals of Artificial Intelligence and Machine Learning.
• Master the basics of digital image processing and its role in facial recognition.
• Develop proficiency in using AI tools and frameworks to build facial recognition models.
• Gain practical experience in creating, training, and testing facial recognition systems.
• Comprehend ethical considerations and best practices regarding the use of facial recognition technology.

This instructor-led, live training in Sweden (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, available Stockholm (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 instructor-led live training in Stockholm (online or onsite) is intended for intermediate to advanced developers, researchers, and data scientists who want to learn how to implement real-time object detection using YOLOv7.

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

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

Computer Vision is a domain focused on the automatic extraction, analysis, and comprehension of valuable insights from digital media. Python stands out as a high-level programming language, renowned for its clean syntax and code readability.

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

Upon completing this training, participants will be able to:

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

Audience

• Python developers interested in Computer Vision

Course Format

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