Fundamentals of Deep Learning

Deep learning powers many of today’s most impactful AI applications, from image recognition to large language models. This NVIDIA DLI course provides a practical introduction to the field through hands-on exercises in computer vision and natural language processing. Participants learn to build and train neural networks from scratch, apply data augmentation and regularization to improve accuracy, and make use of state-of-the-art pre-trained models via transfer learning.

Further information about this tutorial can be found on the NVIDIA DLI course page.

Level: Beginner

Language: English

Price and Eligibility: Refer to the registration page for each event (generally free of charge for members of academia from Europe).

Knowledge

  • Python 3 programming experience, including functions, loops, dictionaries, and arrays
  • Familiarity with Pandas data structures and basic statistics (e.g., computing a regression line)

Technical

  • A free NVIDIA developer account

After completing this course, you will be able to:

  • Build and train neural networks from scratch for computer vision tasks
  • Apply convolutional neural network (CNN) architectures to image classification problems
  • Use data augmentation techniques to improve model generalization with limited data
  • Leverage transfer learning with pre-trained models to achieve strong results efficiently
  • Apply pre-trained large language models to text-based question answering tasks
  • Design and execute a complete deep learning project from data preparation to model evaluation

  • Mechanics of deep learning: training a first model, convolutional neural networks, data augmentation
  • Pre-trained models and large language models: image classification with transfer learning, LLMs for text tasks
  • Final project: color image classification with small datasets, combining transfer learning and feature extraction

  • 2026, Sep 21: full-day online course (Register)

  • 2026, Mar 25: full-day online course
  • 2026, Jan 9: full-day on-site course at NHR@FAU

For an overview of all NHR@FAU courses, visit the course overview page.