Fundamentals of Deep Learning

Wednesday Sep 29, 2021, 9:00 AM → 5:00 PM Europe/Berlin

Together with Nvidia, we are offering a workshop on fundamentals of deep learning.

The Workshop will take place virtually. Participants will be provided with access to a suitable GPU setup.

Connection details for the workshop as well as required steps for access to the setup will be sent to the participants prior to the event.

Learning Objectives

By participating in this workshop, you’ll:

• 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

Prerequisites

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

Suggested materials to satisfy prerequisites: Python Beginner’s Guide.

 

This workshop is part of the Nvidia Deep Learning Institute, further details can be found at https://www.nvidia.com/en-us/training/instructor-led-workshops/fundamentals-of-deep-learning/

 

9:00 AM → 9:15 AM Introduction

Meet the instructor.
Create an account at courses.nvidia.com/join

9:15 AM → 11:15 AM The Mechanics of Deep Learning

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.

11:15 AM → 12:15 PM Break

12:15 PM → 2:15 PM Pre-trained Models and Recurrent Networks

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.
Train a model to autocomplete text based on New York Times headlines.

2:15 PM → 2:30 PM Break

2:30 PM → 4:30 PM Final Project: Object Classification

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.

4:30 PM → 4:45 PM 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.