Introduction to Deep Learning

The following material was used during the Data Mining course of the faculty of Statistics at University of Padua.

Presentations

Below there are the slides used for presenting the lecture during the following conferences:

• 19^th May 2017
• 11^th May 2018
• 24^th May 2019
• 22^nd May 2020

Notebooks

Below there are 2 case studies for a beginning approach to the deep learning. The notebooks are implemented in R programming language, using the keras package:

• MNIST case study: image recognition use case, solved using a convolutional neural network [eng | ita].
• IMDB case study: sentiment analysis use case, solved using a recurrent neural network [eng | ita].

References

• Efron, Bradley, and Trevor Hastie. Computer age statistical inference. Vol. 5. Cambridge University Press, 2016.
• Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.

• François, Chollet. “Deep learning with Python.” (2017).

• THE MNIST DATABASE of handwritten digits.

• Large Movie Review Dataset.

• Aere, Alessandro. “Proprietà statistiche di modelli per il deep learning.” (2017).