The course offers an introduction to multilevel neural networks, back propagation mechanism and training by gradient descent. It then delves into convolutional architectures (CNN) for computer vision and recurrent networks (RNN, LSTM, GRU, Transformer) for time series and NLP.
Target audience:
This course is designed for PhD students in Computer Science and students in master’s programs in related disciplines (AI, Data Science, Computer Engineering). A basic knowledge of linear algebra, statistics and programming in Python is recommended. Suitable for those who wish to acquire practical and theoretical skills in the field of Deep Learning.