diff --git a/README.md b/README.md
index 3ed0c3e..a71c261 100644
--- a/README.md
+++ b/README.md
@@ -2,3 +2,17 @@
 neural networks to learn Koopman eigenfunctions
 
 Code for the paper "Deep learning for universal linear embeddings of nonlinear dynamics" by Bethany Lusch, J. Nathan Kutz, and Steven L. Brunton
+
+To run code:
+
+1. Clone respository.
+2. In the data directory, recreate desired dataset(s) by running DiscreteSpectrumExample, Pendulum, FluidFlowOnAttractor, and/or FluidFlowBox in Matlab. (or email to ask for the datasets)
+3. Back in the main directory, run desired experiment(s) with python.
+
+Notes on running the Python experiments:
+- A GPU is recommended but not required. The code can be run on a GPU or CPU without any changes.
+- The paper contains results on the four datasets. These were the best results from running scripts that do a random parameter search (DiscreteSpectrumExampleExperiment.py, PendulumExperiment.py, FluidFlowOnAttractorExperiment.py, and FluidFlowBoxExperiment.py). 
+- To train networks using the specific parameters that produced the results in the paper instead of doing a parameter search, run DiscreteSpectrumExampleExperimentBestParams.py, PendulumExperimentBestParams.py, FluidFlowOnAttractorExperimentBestParams.py, and FluidFlowBoxExperimentBestParams.py.
+- The experiment scripts include a loop over 200 random experiments (random parameters and random initializations of weights). You'll probably want to kill off the script earlier than that!
+- Each random experiment can run up to params['max_time'] (in these experiments, 4 or 6 hours) but may be automatically terminated earlier if the error is not decreasing enough. If one experiment is not doing well, the script moves on to another random experiment.
+- If the code decides to end an experiment, it saves the current results. It also saves every hour.