More about the event
Workshop „Deep Learning - Basic Tool in Artificial Intelligence “
The workshop will cover basic ideas, approaches, techniques, and applications of Neural Networks (NN), Deep Learning (DL), and Artificial Intelligence (AI) and it is aimed at all interested in using these tools in their field of interest. It will cover the core techniques used today and in particular, it plans to introduce error backpropagation algorithm (EBP), single layer NNs and their extensions to multilayers learning structures dubbed DL. The course will combine interactive lectures, exercises and Python code demonstrations aiming at mastering the concepts and get acquainted with the tools which would later enable all the participants to use them in their applications and projects. The workshop will not be focused on specific applications but rather give a broad introduction to the exciting new ideas and approaches in AI. The program includes hands-on sessions, demonstrating practical aspects.
All activities will be online – on the Microsoft Teams platform.
We are looking forward to seeing you participate in this workshop.
Professor Tomasz Arodz, prof.dr.sc., Virginia Commonwealth University, Richmond, VA
Professor Vojislav Kecman, prof.dr.sc., VsiTe, College for Information Technologies, Zagreb
Lectures Ninoslav Čerkez, VsiTe, College for Information Technologies, Zagreb
Introduction Workshop - Python and PyTorch – setup and introduction
Introduction Day, Friday, 30 April 2021
Python, installation and setup
Basic data types, data structures, control and iterative structures, functions in Python
Object features in Python
PyTorch, installation and setup
Basic of deep learning in PyTorch
Day 1. Tuesday, 04 May 2021
Basics of Machine Learning
Supervised vs Unsupervised Learning
Feed-Forward Neural Networks
Error Back Propagation
Bias and Variance in Neural Networks
Automated Differentiation (AD)
PyTorch example of Automated Differentiation
Problems in training Deep Networks and how to overcome them.
Convolutional Neural Networks (CNNs)
Residual Networks (ResNets)
Self-attention-based Networks (Transformers)
Day 2. Wednesday, 05 May 2021
Building deep networks from modules in PyTorch
PyTorch example of Convolutional Deep Network for image recognition
PyTorch example of an Attention-based Deep Network for language tasks
TensorFlow as an alternative to PyTorch
Please consider that the required prerequisites are: at least basic knowledge of Python, a basic understanding of matrix algebra, and single variable calculus.