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November 11 @ 6:00 pm - 7:30 pm

Join us for an interactive webinar where we will delve into the YOLOv10 architecture, highlighting its enhancements and applications in kidney stone detection. This hands-on session is designed to equip participants with the skills needed to set up their environment in Google Colab and prepare a dataset, covering essential tasks such as loading, preprocessing, and splitting data.

During the webinar, attendees will engage in fine-tuning the YOLOv10 model. You will load a pre-trained model and train it on a custom dataset while monitoring performance metrics in real time. We will evaluate the model’s predictions on unseen data and discuss effective deployment strategies for real-world applications.

This webinar is designed for a diverse group of participants, including data scientists and machine learning engineers who are eager to deepen their knowledge in computer vision and object detection. Medical professionals and researchers focusing on medical imaging and diagnostics will find valuable insights applicable to their fields. Additionally, students and educators in AI and machine learning courses, as well as developers looking to enhance their skills in model fine-tuning and deployment, will benefit from the practical, hands-on experience offered in this session.

 

 

Additional Details:

  • Hands-On Participation: We encourage all participants to bring their laptops and actively engage in the coding exercises throughout the session for a fully immersive learning experience.
  • Prerequisites: A foundational understanding of Python and basic machine learning concepts will help you get the most out of this webinar.
  • Post-Webinar Resources: Attendees will receive valuable resources after the session, including sample code and links to datasets, enabling you to continue your learning journey.
 

Don’t miss this opportunity to deepen your understanding of AI and machine learning in a collaborative environment!

 

 

Webinar Details:

📅 Date: November 11, 2024
⏰ Time: 6h00 PM to 7h30 PM (GMT+2)
📍 Location: Online (Zoom)

Join us and take a step closer to your professional goals!