Fundamentals of Deep Learning (NVIDIA), Research Computing Bootcamp

Fri, Jan 29, 2021, 9:00 am
Speaker(s): 
Sponsor(s): 
Center for Statistics and Machine Learning
Princeton Institute for Computational Science & Engineering (PICSciE) and OIT Research Computing

Full event details and registration link here.

Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers can learn and recognize patterns from data that are considered too complex or subtle for expert-written software.

In this day-long workshop, you will learn how deep learning works through hands-on exercises in computer vision and natural language processing. Both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) will be discussed. You’ll then train deep learning models from scratch, learning tools and tricks to achieve highly accurate results. 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.

Learning objectives

Participants will:

  • 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 their own projects with a modern deep-learning framework

Knowledge prerequisites

No prior GPU programming knowledge is required. However, participants should be at least conversant in Python and understanding the syntax and implementation of fundamental programming concepts in Python (e.g. functions, loops, dictionaries, arrays).

The workshop will make use of Python packages such as NumPy, Pandas, Tensorflow, and Keras. Some basic experience with at least NumPy and Pandas is recommended.

Hardware/software prerequisites

Participants only need a desktop or laptop computer capable of running the latest version of Chrome or Firefox. There are no other hardware requirements, as each participant will be provided with dedicated access to a fully configured, GPU-accelerated workstation in the cloud.

Some communication during the workshop may happen over Slack. Participants should either have a Slack client installed or their laptops or be prepared to use Slack within a browser.

Session format

Lecture, demonstration, and hands-on labs and exercises


Questions? Contact rcinfo@princeton.edu.