


Convolutional Neural Networks Lecture
Convolutional Neural Networks form the backbone of current AI revolution and are used in a multitude of classification and regression problems. This lecture overviews the transition from multilayer perceptrons to deep architectures.



Graph Convolutional Networks
This lecture overviews Graph Convolutional Networks (GCN) that have many applications in Deep Learning, Signal and Video Analysis, Network Theory, Web Science and Social Media Analytics.



MultiDrone Datasets
This lecture overviews MultiDrone Datasets that has many applications in autonomous drone research and development.



Natural Language Processing
This lecture overviews Natural Language Processing (NLP) that has many applications in text analytics, Linguistics, Machine translation and sentiment analysis.



Neural Speech Recognition
This lecture overviews Neural Speech Recognition is a special case of Automatic Speech Recognition (ASR), i.e., the transcription of speech to text that has many applications e.g., in call centers, dictation, meeting minutes creation, Smart assist



Recurrent Neural Networks. LSTMs
This lecture overviews Recurrent Neural Networks and Long Short-Term Memory (LSTM) networks that have many applications in signal and video analysis. It covers the following topics in detail: Neural Networks for Sequence Analysis.