This is currently a work in progress!

• Do you not know what a neural network is? Use scikit-learn.
• Do you want to use only the tried and tested elements of deep learning? Use Caffe.
• Do you want to create novel architectures? Use Lasagne.
• Do you want to create novel architectures that are inelegant to do in Lasagne? Use Lasagne.
• Have you created these novel architectures that are inelegent to do in Lasagne? If you can live with it, keep using Lasagne.
• Are they inelegent because they are recurrent? Try blocks.
• Still not satisfied? Maybe this is a good fit. (:

• required
  • numpy
  • theano
  • networkx
  • six
  • toolz
• optional
  • lasagne (recommended - for conv/pooling layers)
  • pydot
  • pygraphviz
  • scikit-learn (for the examples)

• nose
• sniffer
  • optional, to auto-run nosetests

• DAGano doesn’t have the same ring to it
• architectures are constructed as immutable trees, and this allows you to customize the behavior of subtrees instead of manipulating a single global network
  • principle of locality: it’s more likely that you’ll want close-by nodes to behave similarly - thus having subnetworks makes sense
  • immutability is a good means of managing complexity - thus a tree makes sense