Fundamentals of Deep Learning

8 hours of instruction

Learn how deep learning (DL) works through hands-on exercises in computer vision and natural language processing (NLP). You will train deep learning models from scratch, and pick up tricks and tools for achieving highly accurate results along the way. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly

OBJECTIVES

1. Learn the fundamental techniques and tools required to train a deep learning model
2. Gain experience with common deep learning data types and model architectures
3. Enhance datasets through data augmentation to improve model accuracy
4. Leverage transfer learning between models to achieve efficient results with less data and computation
5. Build confidence to take on your own project with a modern deep learning framework

PREREQUISITES

None

SYLLABUS & TOPICS COVERED

1. Introduction
   • Meet the instructor
   • Create an account
2. The Mechanicsof Deep Learning
   • 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
3. Pre Trained Models And Recurrent Networks
   • 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
4. Final Project Object Classification
   • 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
5. 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.

SOFTWARE REQUIREMENTS

Each participant will be provided with dedicated access to a fully configured, GPU-accelerated workstation in the cloud.