ΠΕΡΙΓΡΑΦΗ

Businesses worldwide are increasingly turning to artificial intelligence (AI) to tackle a range of challenges. Deep learning, a robust AI methodology, employs multi-layered artificial neural networks to achieve exceptional accuracy in audio, visual, or text-related tasks. This training provides hands-on experience with applying deep learning techniques in computer vision and natural language processing, guiding participants through training state-of-the-art models and efficiently utilizing them for various applications.

This is a hybrid event, so you can either attend physically or participate online:
1st floor, Agias Fylaxeos 92, Limassol Campus +  online (login information will be provided after registering)

Participants will receive a signed Certificate-of-Attendance issued by the Executive Education Center of the University of Limassol upon completion of the course. 

ΣΚΟΠΟΣ ΣΕΜΙΝΑΡΙΟΥ

Seminar Objectives:

Insight into the Background of Deep Learning:

• Getting to know AI-based workloads and fundamental principles.
• Acquiring essential skills and tools for training deep learning models and gaining hands-on experience with prevalent data types and model architectures.

Getting Familiar with Common Deep Learning Tasks:

• Understanding the structure and operation of artificial neural networks.
• Solving computer vision-based and natural language processing problems with dedicated architectures.
• Having skills to apply transfer learning and augmentation techniques.

Experiencing to Implement Deep Learning Models:

• Gaining knowledge to implement your future solutions via hands-on exercises.
• Getting to know how the current large AI models can efficiently solve challenging vision and text-based tasks.

The primary aim of the training is to introduce participants to the current deep learning techniques through practical examples. To this end, we will briefly review the basics of responsible AI, the main technical concepts, the most common types of tasks, and the architectures that solve them. We also cover the latest state-of-the-art technologies. During the workshop, we expect active participation, so that through the prepared hands-on exercises, the participants will have the opportunity to independently create implementations solving specific tasks under instructor supervision.

The Jupyter notebooks considered for the hands-on training will be available to registered participants.

ΣΕ ΠΟΙΟΥΣ ΑΠΕΥΘΥΝΕΤΑΙ

• Business peers to see the capabilities of deep learning in various fields.
• Students to start their journey in modern AI.
• Everyone to better understand how and why AI may change our world.

ΠΕΡΙΣΣΟΤΕΡΕΣ ΠΛΗΡΟΦΟΡΙΕΣ

Key Topics Covered:

An Introduction to Modern AI and Deep Learning:

• Everyday human routine activities and how artificial intelligence can enhance our ability to perform them; the common AI workloads.
• The main principles that should be considered for responsible AI-based software development; challenges and risks regarding AI.
• The basic principles of machine learning. Main tasks, fitting and evaluating corresponding models.

Basic Neural Network Architectures:

• Building your first neural network
• Solving image analysis problems with convolutional neural networks.
• Applying recurrent neural networks to a natural language processing (NLP) task.

Complementary Techniques for AI Models:

• Incorporating data augmentation into your model to extend small training datasets.
• Using pre-trained models to directly solve your problem or fine-tuning them further for your data.

State-of-the-Art Advanced Architectures:

• Fast and accurate models to solve practical object detection problems.
• Transformer-based large language models (LLMs) for text analysis.

Trainers

Dr András Hajdu (AI & Deep Learning Expert): 

András Hajdu received an MSc degree in Mathematics from the University of Debrecen, Hungary, in 1996. He obtained his PhD degree in Mathematics and Computer Science in 2003. He worked as a PostDoc researcher for the Artificial Intelligence Information Analysis Laboratory, Dept. of Informatics, Aristotle University of Thessaloniki between 2005-2006. Since 2017 he has been a full professor, since 2011 the head of the Department of Data Science and Visualization, and since 2019 the dean of the Faculty of Informatics, University of Debrecen.

He is the leader of the local Data Science and Visualization doctoral program and the founder and director of the Gyorgy Hajos Data Science Students College. He worked as a data scientist for the Hungarian Data Asset Agency between 2019-2022.

He serves as an instructor for the Microsoft Learn for Educators program and is a certified NVIDIA instructor and university ambassador. He has co-authored 57 journal papers and 120 conference papers cited more than 3500 times; his H-index is 29. His main interest lies in data science/artificial intelligence, medical image processing, and discrete mathematics.

Gergő Bogacsovics (AI & Deep Learning Expert):

Gergo Bogacsovics is a PhD student at the Faculty of Informatics of the University of Debrecen, Hungary. He received his Bachelor of Science and Master of Science in Computer Science from the University of Debrecen, Hungary. He has 5+ years of teaching experience in the fields of artificial intelligence, machine- and deep learning, as well as reinforcement learning and natural language processing. He is also a certified NVIDIA DLI instructor and ambassador.

His main research areas include developing novel machine- and deep learning-based solutions in the clinical domain. He also has substantial industry experience in the Data Analysis and Data Engineering fields, and has worked in diverse cultural environments, both with European and non-European partners.