Fundamentals of Deep Learning

This two-part workshop will introduce deep learning techniques and applications. 

Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers can learn and recognize patterns from data that are considered too complex or subtle for expert-written software. Each session is roughly divided into 3 hours of active learning and 1 hour of extra Q&A. You will work with Python code in a Jupyter notebook environment.

Learning objectives:



Learn the fundamental techniques and tools required to train a deep learning model





Gain experience with common deep learning data types and model architectures





Enhance datasets through data augmentation to improve model accuracy





Leverage transfer learning between models to achieve efficient results with less data and computation


Upon successful completion of the assessment, you will receive an NVIDIA certificate.