Developing a scalable CNN for Building Damage Classification

We are excited to announce our first webinar on “Developing a scalable CNN for building damage classification” on March 28th at 12 - 2 PM Central Time. This webinar will guide you through building and deploying effective Convolutional Neural Networks (CNNs) for automated building damage identification. We’ll cover image classification techniques, CNN fundamentals, and hands-on experience to empower you with the skills to scale your solutions.

Please Register for the Webinar: https://www.chishiki-ai.org/posts/2024-webinar-cnn-building-damage/

Key Topics:

• Understanding Image Classification and CNNs: Explore basic machine learning models, Multi-layer Perceptrons (MLPs), and the core components of CNNs for image analysis.
• The Building Blocks of CNNs: Delve into the essential elements of CNNs, including convolutional layers, pooling, activation functions, and more.
• Training Deep Learning Models: Learn strategies for effectively training your models, including data augmentation and transfer learning techniques.
• Workflow for Deep Learning: Gain practical knowledge with a step-by-step deep learning workflow.
• Case Study: Natural Hazard Detection: See real-world applications of CNNs for building damage identification after natural disasters.
• Hands-on Session: Get hands-on experience building and deploying a CNN for damage classification using PyTorch.

Pre-requisites:

• Please register to have a TACC account if you don’t already have one: https://accounts.tacc.utexas.edu/register
• Basic understanding of Python programming is recommended but not essential.

Connect with us on LinkedIn  https://www.linkedin.com/company/chishikiai/