This is a GitHub repository of (1) paper summaries, (2) lists of developments and (3) glossaries related to deep learning.

The idea is to write (or link to) paper summaries, blog posts or articles that will help people (especially those starting out):

• Understand what different models or terms mean
• Know what the state-of-the-art results are in each domain
• Be able to look up known advantages and disadvantages of key models and approaches
• See how different concepts connect with each other

It is thus crucial that

• these summaries are presented in a way that makes the relationships between different concepts clear (hence this being a ‘map’), and that
• materials are chosen selectively so as not to overwhelm the reader.

This is still in early stages – at the moment this is more a collection of paper summaries and definitions of terms than a map. It also contains material outside of deep learning, mostly in machine learning or neuroscience.