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.
