Compared to Cloud Computing, which is centralized in computing and data storage, Edge Computing brings computation and data storage closer to data sources.
Edge AI combines edge computing and AI technology and has become a rapidly developing field in the past few years. Edge AI enables AI computing directly on the edge or client device, enhancing power efficiency, supporting low latency, and solving data privacy problems.
Therefore, what improvements need to be made to traditional deep learning algorithms in Edge AI scenarios? This course teaches you about deep model compression and optimization techniques, decentralized and collaborative deep learning approaches and algorithms, software, and hardware for Edge AI.
Language: English
Deutsch, English
Big Data and AI, Cloud and Operating Systems, Intermediate
Course information
What will you learn?
- Summary of deep learning basics relevant for this course
- Deep model compression and optimization techniques
- Decentralized and collaborative deep learning
- Algorithms, software and hardware for Edge AI
Is this course for me?
Prerequisites
- High school math is required (pre-course)
- Basics in Machine Learning and Deep Learning
- Python as programming language
The German MOOC Praktische Einführung in Deep Learning für Computer Vision teaches relevant basics regarding Deep Learning and Computer Vision. Unfortunately we did not find a fitting English alternative.
Level
Advanced
Time required
6-8 hours per week
How do I get started?
Environment setup (or what you'll need for our practical examples)
- A Webbrowser (preferably on a laptop or desktop computer)
- A Kaggle account (we will do all coding exercises using Kaggle Notebooks)
Attention: This course is currently in self-study mode, in which you do not have access to graded assignments/exams. Therefore, we can only issue you a certificate of participation.
Enroll me for this course
Learners
Certificate Requirements
- Gain a Record of Achievement by earning at least 50% of the maximum number of points from all graded assignments.
- Gain a Confirmation of Participation by completing at least 50% of the course material.
Find out more on our page for certificates and guidelines.
This course is offered by
Haojin Yang is a senior researcher and multimedia and machine learning (MML) research group leader at Hasso-Plattner-Institute (HPI). Since 2019, he has been habilitated for a professorship. His research focuses on efficient deep learning, model acceleration and compression, and Edge AI.
Joseph Bethge is a PhD student studying deep learning techniques in the area of Computer Vision with a focus on Binary Neural Networks (BNNs) and is one of the main authors of the Open-Source framework BMXNet 2 for BNNs.