CapsNets are a hot new architecture for neural networks, invented by Geoffrey Hinton, one of the godfathers of deep learning.

NIPS 2017 Paper:
* Dynamic Routing Between Capsules,
* by Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton
* https://arxiv.org/abs/1710.09829

The 2011 paper:
* Transforming Autoencoders
* by Geoffrey E. Hinton, Alex Krizhevsky and Sida D. Wang
* https://goo.gl/ARSWM6

CapsNet implementations:
* Keras w/ TensorFlow backend: https://github.com/XifengGuo/CapsNet-Keras
* TensorFlow: https://github.com/naturomics/CapsNet-Tensorflow
* PyTorch: https://github.com/gram-ai/capsule-networks

Book:
Hands-On Machine with Scikit-Learn and TensorFlow
O'Reilly, 2017
Amazon: https://goo.gl/IoWYKD

Github: https://github.com/ageron
Twitter: https://twitter.com/aureliengeron

Slides:
https://www.slideshare.net/aureliengeron/introduction-to-capsule-networks-capsnets

Errata:
* At 15:47, in the margin loss equation, the max should be squared, but not the norm: L_k = T_k max(0, m+ − ||v_k||)² + λ (1 − T_k) max(0, ||v_k|| − m−)². Therefore, at 16:08, the network should output a vector whose length (not squared length) is longer than 0.9 for digits that are present, or smaller than 0.1 for digits that are absent. I'll clarify this point in my next video on implementing Capsule Networks.