1. This repository follows the great tutorial by [Eric Jang on normalizing flows.](https://github.com/ericjang/normalizing-flows-tutorial)
2. Repository will update itself as variations of normalizing flows are implemented.
3. Performance evaluation will be done at the end of the project.
4. Implementations are in Jupyter Notebook. Articles on normalizing flows, possibly with modular Python scripts of notebooks, will be added on the fly.
### Implementation
* Parameters
* number of hidden layers
* base distribution
* bijector count
* neuron size
* optimizers
* iteration count
### Performance Evaluation
* Performance evaluation will be done at the end of the project.
* convergence time
* correctness
* robustness
### Reference
* This repository is inspired by the great tutorial by [Eric Jang on normalizing flows](https://github.com/ericjang/normalizing-flows-tutorial) and [TensorFlow probability docs.](https://www.tensorflow.org/probability)
