Update README.md

README.md

@@ -8,25 +8,34 @@
 * Scripts
   * modular python implementations of flows
 
-### Implementation [Planned, may differ -- use inline with your expectations]
+* Noisy moons
+  * initial test data; [see usage.](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html)
 
-* Parameters
+### Implementation [subject to project scope]
+
+* Parameters [subject to project scope]
   * number of hidden layers
-  * base distribution -- [Gaussian converges better than uniform in most experiments]
+  * base distribution -- [gaussian converges better than uniform in most experiments]
   * bijector count
   * neuron size
   * optimizers 
   * iteration count
   
-### Performance Evaluation  [Planned, may differ -- use inline with your expectations]
+### Performance Evaluation [subject to project scope]
 * Performance evaluation will be done at the end of the project.
   * convergence time
   * correctness
   * robustness
 
+### Updates 
+* [Discard experiments directory.](https://github.com/kaanguney/normalizing_flows/tree/main/notebooks/experiments)
+* [Preprocessing currently supports `prostate.xls`.](https://github.com/kaanguney/normalizing_flows/tree/main/scripts/preprocessing)
+* [Refer to noisy-moons directory]() for the most recent, most visual implementation.
+
 ### References
 * Rezende, D. J., & Mohamed, S. (2015). [Variational Inference with Normalizing Flows.](https://arxiv.org/abs/1505.05770v6)
 * Kobyzev, I., Prince, S. J. D., & Brubaker, M. A. (2019). [Normalizing Flows: An Introduction and Review of Current Methods.](https://arxiv.org/abs/1908.09257v4)
+* [Probabilistic Deep Learning with TensorFlow 2 by Imperial College London](https://www.coursera.org/learn/probabilistic-deep-learning-with-tensorflow2)
 * Blog posts
  * [Eric Jang](https://github.com/ericjang/normalizing-flows-tutorial)
  * [Lilian Weng](https://lilianweng.github.io/lil-log/2018/10/13/flow-based-deep-generative-models.html)