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awesome-capsule-networks - A curated list of awesome resources related to capsule networks

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A curated list of awesome resources related to capsule networks maintained by AI Summary. Please pull a request if you are aware of additional resources.

Hands-On-Deep-Learning-Algorithms-with-Python - Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow

  •    Jupyter

Deep learning is one of the most popular domains in the artificial intelligence (AI) space, which allows you to develop multi-layered models of varying complexities. This book is designed to help you grasp things, from basic deep learning algorithms to the more advanced algorithms. The book is designed in a way that first you will understand the algorithm intuitively, once you have a basic understanding of the algorithms, then you will master the underlying math behind them effortlessly and then you will learn how to implement them using TensorFlow step by step. The book covers almost all the state of the art deep learning algorithms. First, you will get a good understanding of the fundamentals of neural networks and several variants of gradient descent algorithms. Later, you will explore RNN, Bidirectional RNN, LSTM, GRU, seq2seq, CNN, capsule nets and more. Then, you will master GAN and various types of GANs and several different autoencoders.




capsule-net-pytorch - A PyTorch implementation of CapsNet architecture in the NIPS 2017 paper "Dynamic Routing Between Capsules"

  •    Python

The current test error is 0.21% and the best test error is 0.20%. The current test accuracy is 99.31% and the best test accuracy is 99.32%. A Capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or object part.

CapsNet_Tensorflow

  •    Python

This repository contains different tests performed on a capsule network model. Example code to train the capsule_dynamic(CapsNet with dynamic routing) model on mnist dataset.

capsnet-traffic-sign-classifier - A Tensorflow implementation of CapsNet(Capsules Net) apply on german traffic sign dataset

  •    Jupyter

This implementation is based on this paper: Dynamic Routing Between Capsules (https://arxiv.org/abs/1710.09829) from Sara Sabour, Nicholas Frosst and Geoffrey E. Hinton. During the training, the checkpoint is saved by default into the outputs/checkpoints/ folder. The exact path and name of the checkpoint is print during the training.

Hands-On-Deep-Learning-Algorithms-with-Python - Hands-On Deep Learning Algorithms with Python, By Packt

  •    Jupyter

This is the code repository for Hands-On Deep Learning Algorithms with Python, published by Packt. Deep learning is one of the most popular domains in the AI space, allowing you to develop multi-layered models of varying complexities.







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