Fundamentals of Deep Learning

Course Description

By the end of this workshop, participants have experienced how deep learning works through hands-on exercises in computer vision and natural language processing. They can train deep learning models from scratch and know about tools and tricks to achieve highly accurate results. They can also leverage freely available, state-of-the-art pre-trained models to save time and get their deep learning applications up and running quickly.

Additional information is available on the Nvidia DLI course homepage.

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

Course Structure

The Mechanics of 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.

Pre-trained Models and Large Language Models

  • 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.

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.

Certification

Upon successfully completing the course assessments, participants will receive an NVIDIA DLI Certificate, recognizing their subject matter expertise and supporting their professional career growth.

Prerequisites

A free NVIDIA developer account is required to access the course material. Please register before the training at https://learn.nvidia.com/join.

Participants should additionally meet the following requirements:

  • An understanding of fundamental programming concepts in Python 3, such as functions, loops, dictionaries, and arrays
  • Familiarity with Pandas data structures
  • An understanding of how to compute a regression line.

Upcoming Iterations and Additional Courses

You can find dates and registration links for this and other upcoming NHR@FAU courses at https://hpc.fau.de/teaching/tutorials-and-courses/.