Introduction to Neural Networks and Deep Learning
Neural networks have become extremely popular and powerful in the
last decade, solving complex problems that were until recently in the
exclusive domain of humans, such as game-playing, image
recognition/generation, speech recognition/generation, among others.
Practical information:
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- Prerequisites: basic knowledge of Python
- Price: See registration link for details
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In this course, we will learn the basic principles upon which neural network models are built, the algorithm that is commonly used to train the models, the different types of architectures that can be designed, and the hyperparameters that we can choose to fit different types of problems. In the practical part of the course, we will also learn to implement and train neural networks using the popular Tensorflow library, in Python.
Teacher/speaker
Daniel Peralta
Dr. Daniel Peralta is a post-doctoral researcher at the Department of Applied Mathematics, Computer Science and Statistics of the Faculty of Sciences of Ghent University. He obtained his PhD at the University of Granada (Spain), tackling large-scale fingerprint identification.
His research has focused on machine learning, especially in large-scale scenarios, and has involved several collaborations with industry to apply such techniques on problems ranging from railway maintenance scheduling to compound activity prediction. Within his current position at the VIB, this research is applied on biological data. He currently teaches Big Data Science courses at Ghent University, in the Master of Statistical Data Analysis and the Master in Computer Science.
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