Introduction > Meet the instructor. > Create an account at courses.nvidia.com/join

The Mechanics of Deep Learning 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 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 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 > 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.