Deep Learning Social Sciences

Deep learning is all around us, in face recognition, natural language processing, and in many other places. Deep means that data are represented through multiple layers. This introduces a great deal of flexibility in the model, with the effect that predictions become much better.

In the social sciences, too, deep learning is starting to be used. This is not surprising because images, text, and video are common in the social science disciplines. Thus, learning deep learning techniques is gradually becoming an essential data analytic skill.

This course introduces the most important algorithms and applications of deep learning. It does so using Python, arguably the most important programming language for deep learning. We discuss the basics of deep learning, including tensors. We then move to convolutional, recurrent, and adversarial networks, as well as restricted Boltzmann machines. We apply the methods to images, text, time series, and choice data. The course alternates between lectures and hands-on programming sessions.

References

This repository follows the book: Chollet, François (2021). Deep learning with Python. Second edition. OCLC: on1289290141. Shelter Island: Manning Publications.

The first and the second labs are about python and data wrangling, they are a merge or different sources, such as:

• https://github.com/jakevdp/WhirlwindTourOfPython/tree/6f1daf714fe52a8dde6a288674ba46a7feed8816
• https://swcarpentry.github.io/python-novice-inflammation/

Class 2 (W2) : Introduction to Python

Class 3 (W3): Data wrangling: numbers, text and images

Class 6 (W6): Programming feed-forward neural networks

Class 7 (W7): Working with Keras

Class 9 (W10): Convolutional neural networks for image detection

Class 11 (W12): Recurrent networks for time series and text analysis

Class 13 (W14): Generative adversarial networks (GANs)