Fundamentals of Deep Learning

Course Description & Learning Outcomes

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

• Build confidence to take on your own project with a modern deep learning framework

Topics Covered

• PyTorch

• Convolutional Neural Networks (CNNS)

• Data Augmentation

• Transfer Learning

• Natural Language Processing

Course Outline

The below is a suggested timeline for the course. Please work with the instructor to find the best timeline for your session.

Introduction (30 mins)

• Meet the instructor.

• Create an account at courses.nvidia.com/join

The Mechanics of Deep Learning (3 hours)

• 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.

Break (1 hour)Pre-trained Models and Large Language Models (1.5 hours)

• 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.

• Use a Large Language Model (LLM) to answer questions based on provided text.

Break (15 mins)Final Project: Object Classification (1 hours)

• 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 (30 minutes)

• 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.