The Mechanics of Deep Learning

• Abubakrelsedik Karali (NVIDIA DLI)

Room: Virtual Classroom Explore the fundamental mechanics and tools involved in successfully training deep neural networks: - Train your first computer vision model to learn the process of training - Introduce convolutional neural networks to improve accuracy of predictions in vision applications. - Apply data augmentation to enhance a dataset and improve model generalization.

Pre-trained Models and Recurrent Networks

• Abubakrelsedik Karali (NVIDIA DLI)

Room: Virtual Classroom Leverage pre-trained models to solve deep learning challenges quickly. Train recurrent neural networks on sequential data : - Integrate a pre-trained image classification model to create an automatic doggy door. - Leverage transfer learning to create a personalized doggy door that only lets in your dog. - Train a model to autocomplete text based on New York Times headlines.

Final Project : Object Classification

• Abubakrelsedik Karali (NVIDIA DLI)

Room: Virtual Classroom Apply computer vision to create a model that distinguishes between fresh and rotten fruit. - Create and train a model that interprets color images. - Build a data generator to make the most out of small datasets. - Improve training speed by combining transfer learning and feature extraction. - Discuss advanced neural network architectures and recent areas of research where students can further improve their skills.

Final Review Room: Virtual Classroom - Review key learnings and answer questions. - Complete the assessment and earn a certificate. - Complete the workshop survey. - Learn how to set up your own AI application development environment.