consider

README.md

@@ -106,7 +106,7 @@ The following steps need to be performed:
 
 1. Organize downloaded waterfalls into categories (e.g. "good", "bad", "failed").
    Use `wut-dl-sort` script.
-   The script them into their respective directories under:
+   The script will sort them into their respective directories under:
 	* `data/train/good/`
 	* `data/train/bad/`
 	* `data/train/failed/`
@@ -143,6 +143,48 @@ mkdir download
 rsync -ultav rsync://ml.spacecruft.org/download/ download/
 ```
 
+# TODO / Brainstorms
+This is a first draft of how to do this. The actual machine learning
+process hasn't been looked at at all, except to get it to generate
+an answer. It has a long ways to go. There are also many ways to do
+this besides using Tensorflow and Keras. Originally, I considered
+using OpenCV. Ideas in no particular order below.
+
+## General
+General considerations.
+
+* Use Open CV.
+
+* Use something other than Tensorflow / Keras.
+
+* Do mirror of `network.satnogs.org` and do API calls to it for data.
+
+* Mirror data/ dir in Apache/rsync too.
+
+## Tensorflow / Keras
+At present Tensorflow and Keras are used.
+
+* Learn Keras / Tensorflow...
+
+* What part of image is being evaluated?
+
+* Re-evaluate each step.
+
+* Right now the prediction output is just "good" or "bad", needs
+  "failed" too.
+
+* Give confidence score in each prediction.
+
+* Visualize what ML is looking at.
+
+* Separate out good/bad/failed by satellite, transmitter, or encoding.
+  This way "good" isn't considering a "good" vetting to be a totally
+  different encoding. Right now, it is considering as good observations
+  that should be bad...
+
+* If it has a low confidence, return "unknown" instead of "good" or "bad".
+
+
 # Caveats
 This is the first machine learning script I've done,
 I know little about satellites and less about radio,