Using PyTorch to Predict Wildfires
Forest fires pose a significant threat to ecosystems, wildlife, and human lives. Given their devastating consequences, the ability to predict forest fires plays a crucial role in mitigating their impact. Traditional methods have long been employed to forecast these fires, but with the advent of machine learning, we now have the potential to enhance our predictive capabilities significantly. In this workshop, we will walk through how to perform image analysis to predict potential forest fire likelihoods based on regions of known forest fires acquired via the MODIS (Moderate Resolution Imaging Spectroradiometer) dataset. We will use the Intel® Extension for PyTorch*, powered by oneAPI, to optimize and accelerate PyTorch-based model finetuning, whereby the pre-trained ResNet model is adapted to aerial photos. We will walk through utility functions, a trainer class, a model class, and a metrics class. Lastly, we will create a final confusion matrix to review the model accuracy.
Speaker(s)
Bob Chesebrough - Technical Evangelist/Intel
Robert Chesebrough is currently a Technical Evangelist in the Intel Developer Academy. His educational background is physics. His industry experience has been software development and application/performance engineering for fortune 100 companies and national laboratories for over three decades. He is a data scientist, using machine learning/ deep learning for eight years while working for Intel and other high-tech companies.
Rahul Unnikrishnan Nair - Machine Learning Architect
Rahul is a Machine Learning Architect at Intel with 10 years of experience in data science, Machine Learning, and applied Deep Learning. He joined Intel after completing his Master’s degree and has contributed to the OpenStack security group. Rahul has expertise in developing Deep Learning and Deep Reinforcement Learning algorithms optimized for Intel CPUs. He has worked on scaling Deep Learning pipelines on the cloud, collaborated with startups and OEMs, and optimized AI workloads on Intel hardware. Rahul has also led projects in developing an AI platform for drug discovery and played a key role in releasing deep learning containers at Intel.
