Introduction to deep learning

Introduction to deep learning

"The objectives of this module are to learn some of the fundamentals of using deep learning for neuroscience"


The estimated time to complete this training module is 3h.

The prerequisites to take this module are:

If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server. If you do not have access to the server and would like to join, please send us an email at school [dot] brainhack [at] gmail [dot] com.

Follow up with your local TA(s) to validate you completed the exercises correctly.


The first portion of this module was presented by Blake Richards during Brainhack School 2020

The video presentation is available here:


  • Watch the video presentation by Blake Richards.
  • Consider these statements/questions and answer them briefly in a saved doc:
    • Give an example of a research question that you could use deep learning to solve.
    • How would deep learning provide an advantage for solving the problem?
    • Give an example of a research question for which deep learning would not be appropriate.
    • What would be a disadvantage of deep learning compared to another method?
  • Follow up with your local TA(s) to validate you completed the exercises correctly.
  • 🎉 🎉 🎉 you completed this training module! 🎉 🎉 🎉

More resources

You can check out the documentation on Pytorch and additional tutorials here. The Deep Learning Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville is also freely available here For more comprehensive look of the deep learning tools and methods please look at the lecture series by Yan LeCun & Alfred Canziani at University of NY is here and for the videos here To learn and solve various deep learning exercises please look at the resources provided at the Kaggle Deep Learning course Or have a look at the lecture and exercises series provided by the Neuromatch Academy Deep Learning Course