Nvidia DLI – Fundamentals of Deep Learning

bg-new

Official instructor-led NVIDIA DLI workshop

Title

Nvidia DLI - Fundamentals of Deep Learning

Lecturer

Dr. Andras Hajdu, hajdu.andras@inf.unideb.hu

Content and organization

Businesses worldwide are using artificial intelligence (AI) to solve their greatest challenges. Healthcare professionals use AI to enable more accurate, faster diagnoses of patients. Retail businesses use it to offer personalized customer shopping experiences. Automakers use it to make personal vehicles, shared mobility, and delivery services safer and more efficient. Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in object detection, speech recognition, and language translation tasks. Using deep learning, computers can learn and recognize patterns from data that are considered too complex or subtle for expert-written software. In this workshop, you’ll learn how deep learning works through hands-on exercises in computer vision and natural language processing. You’ll 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.

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 fewer data and computation,
  • build confidence to take on your own project with a modern deep learning framework.

Level

Introductory

Course Duration

8 hours

Course Type

Short Course

Participation terms

Free of charge for university students and staff. An understanding of fundamental programming concepts in Python such as functions, loops, dictionaries, and arrays is a prerequisite.

Lecture Plan

The Mechanics of Deep Learning (120 mins), Pre-trained Models and Recurrent Networks (120 mins), Final Project: Object Classification (120 mins)

Schedule

February 2, 2023, 9:00–17:00 CET

Language

English

Modality (online/in person):

online

Notes

Upon successful completion of the assessment, the participant will receive an Nvidia Certificate of Competency.

Host Institution
Nvidia Deep Learning Institute, Faculty of Informatics, University of Debrecen, Hungary

Other short courses

10. 04. 2024 Go

Ethics & STICs

01. 03. 2024 Go

Computer Vision

24. 11. 2023 Go

Human Rights Toolbox

21. 02. 2023 Go

Computer Vision

11. 05. 2022 Go

Geometric learning

05. 04. 2022 Go

Computer Graphics

04. 04. 2022 Go

Bayesian Learning

02. 04. 2022 Go

Computer Graphics

31. 03. 2022 Go

Web of Data

28. 03. 2022 Go

Machine Learning

27. 03. 2022 Go

Machine Learning

02. 03. 2022 Go

Player Modeling

28. 02. 2022 Go

Player Modeling

21. 02. 2022 Go

Affective Computing

21. 02. 2022 Go

Machine Listening

21. 02. 2022 Go

Computer Vision

21. 02. 2022 Go

Computer Vision

21. 02. 2022 Go

Self-Driving Cars

21. 02. 2022 Go

Deep Learning

21. 02. 2022 Go

Deep Learning 2

09. 07. 2021 Go

Self-Driving Cars

09. 07. 2021 Go

Computer Vision

09. 07. 2021 Go

Deep Learning

17. 06. 2021 Go

Deep Learning School

17. 06. 2021 Go

Memory Network

02. 06. 2021 Go

Machine Listening

02. 06. 2021 Go

Affective Computing

02. 06. 2021 Go

Deep Learning 2

01. 06. 2021 Go

Computer Vision

Cookie Settings

A AIDA - AI Doctoral Academy may use cookies to remember your login data, collect statistics to optimize the functionality of the site and to perform marketing actions based on your interests.


These cookies are necessary to allow the main functionality of the website and are automatically activated when you use this website.
These cookies allow us to analyze the use of the website, so that we can measure and improve its performance.
Allow you to stay in touch with your social network, share content, send and post comments.

Required Cookies They allow you to personalize the commercial offers that are presented to you, directing them to your interests. They can be own or third party cookies. We warn you that, even if you do not accept these cookies, you will receive commercial offers, but without meeting your preferences.

Functional Cookies They offer a more personalized and complete experience, allow you to save preferences, show you content relevant to your taste and send you the alerts you have requested.

Advertising Cookies Allow you to stay in touch with your social network, share content, send and post comments.