Neural Networks

Here I will be adding my implementations of several neural networks as I learn more about how each one works.

As of right now, the plan is to slowly go through all of the different kinds of neural networks described in Fjodor van Veen's Neural Network Zoo from The Asimov Institute: http://www.asimovinstitute.org/neural-network-zoo/

Thus, I plan to update this project accordingly as follows:

Perceptrons (P)

Feed Forward Neural Networks (FFNN)

Radial Basis Function (RBF)

Hopfield Network (HN)

Markov Chains (MC)

Boltzmann Machines (BM)

Restricted Boltzmann Machines (RBM)

Autoencoders (AE)

Sparse Autoencoders (SAE)

Variational Autoencoders (VAE)

Denoising Autoencoders (DAE)

Deep Belief Networks (DBN)

Convolutional Neural Networks (CNN)

Deconvolutional Neural Networks (DN)

Deep Convolutional Inverse Graphics Networks (DCIGN)

Generative Adversarial Networks (GAN)

Recurrent Neural Networks (RNN)

Long / Short Term Memory (LSTM)

Gated Recurrent Units (GRU)

Neural Turing Machines (NTM)

Bidirectional Recurrent Neural Networks (BiRNN)

Bidirectional Long / Short Term Memory (BiLSTM)

Bidirectional Gated Recurrent Units (BiGRU)

Deep Residual Networks (DRN)

Echo State Networks (ESN)

Extreme Learning Machines (ELM)

Liquid State Machines (LSM)

Support Vector Machines (SVM)

Kohonen Networks (KN)