Welcome#
Welcome to this Jupyter Book on deep learning, designed for students from all around the world. The content covers a broad spectrum of deep learning topics, starting from foundational principles and gradually advancing to more complex applications. Originally developed for the deep learning course in the Brain and Behaviour Master’s programme at Justus-Liebig University of Giessen, this material is not only tailored for Experimental Psychologists* and Cognitive Neuroscientists but is also suitable for anyone interested in deep learning. Whether you are new to the field or looking to deepen your understanding, this book offers valuable insights and practical examples to enhance your learning experience.
Play with notebooks!
The best way to master the material is through hands-on practice! Each Jupyter Notebook can be executed directly in Google Colab by clicking the icon at the top of the notebook. You can also access all materials in our GitHub repository , which you can clone to run locally on your own computer.
Please give feedback!
If you come across any issues, bugs, typos, or anything else, please let us know! You can report them through the issue tracker or simply click the icon at the top of any page and select “open issue”. We also welcome requests for new materials, and if you’d like to contribute, pull requests are highly appreciated.
Table of contents#
Prerequisites
Tutorials
Assignments
Student Projects
Complementary Materials
External materials#
Below is a list of materials that can complement the course content.
Python tutorials#
While the tutorials explain Python codes in great detail, familiarity with Python is required to follow the course. Useful materials for inexperienced Python programmers:
Recommended resources#
There are several online resources covering deep-learning theoretical and practical aspects: