Displaying 152 resources
Geometric deep learning
Unlock the world of Geometric Learning and Graph Convolutional Networks (GCNs) in this comprehensive course designed to empower you with cutting-edge knowledge and practical skills.
Continuous-time Signals and Systems
This lecture overviews continuous-time Signals and Systems topics. Continuous-time signals are presented: periodic signals, delta function, unit step signal, exponential signal, trigonometric signals, complex exponential signal.
Tutorial paper on Deep Learning for Graphs
The adaptive processing of graph data is a long-standing research topic that has been lately consolidated as a theme of major interest in the deep learning community.
Introduction to Autonomous Systems
A fully autonomous system can: a) gain information about the environment, b) work for an extended period without human intervention, c) move either all or part of itself throughout its operating environment without human assistance and d) avoid situa
1D Convolutional Neural Networks
This lecture overviews 1D Convolutional Neural Networks that has many applications in 1D signal analysis.
Road Condition Assessment
This lecture overviews Road Condition Assessment that has many applications in autonomous car perception and smart city management. It covers the following topics in detail:
Artificial Neural Networks. Perceptron
This lecture will cover the basic concepts of Artificial Neural Networks (ANNs): Biological neural models, Perceptron, Activation functions, Loss types, Steepest Gradient Descent, On-line Perceptron training, Batch Perceptron training.
Attention and Transformers Networks
In this lecture, the limitations of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) in effectively processing sequences are emphasized.
Z Transform
This lecture overviews Z Transform that has many applications in signal processing and systems theory.
Laplace Transform
This lecture presents Laplace Transform (LT) and its region of convergence. Its relation to Laplace transform is presented. Notable LT properties are reviewed: time shift, convolution, signal differentiation/integration.
Statistical Detection
This lecture overviews Statistical Detection that has many applications in Machine Learning, Signal Analysis and Statistical Communications.
Fourier Transform
This lecture overviews the topics of continuous-time periodic signals, signal frequencies and Fourier Transform (FT). Its relation to Laplace transform is presented.