Tensorflow Guides
A bunch of implementations of common machine learning architectures, both neural and not neural, using tensorflow. This is meant to be a learning avenue for me as I try to solidify new ideas I read about, while also being a useful resource for others trying to learn more about machine learning, deep learning or tensorflow.
Implemented Models
- Variational Autoencoders with Multinomial Likelihood
in progress - Convolutional Variational Autoencoders
- Probabilistic Matrix Factorization
in progress - Seq2seq Encoder Decoder (with Attention)
in progress