diff --git a/haskell-ml.cabal b/haskell-ml.cabal
--- a/haskell-ml.cabal
+++ b/haskell-ml.cabal
@@ -1,7 +1,28 @@
 name:                haskell-ml
-version:             0.4.0
+version:             0.4.1
 synopsis:            Machine learning in Haskell
-description:         Machine learning in Haskell
+description:         Provides a very simple implementation of deep (i.e. - multi-layer),
+  fully connected (i.e. - _not_ convolutional) neural networks. Hides the type of the
+  internal network structure from the client code, while still providing type safety,
+  via existential type quantification and dependently typed programming techniques,
+  ala Justin Le. (See [Justin's blog post](https://blog.jle.im/entry/practical-dependent-types-in-haskell-2.html).)
+
+  The API offers a single network creation function: `randNet`, which allows the user
+  to create a randomly initialized network of arbitrary internal structure by supplying
+  a list of integers, each specifying the output width of one hidden layer in the network.
+  (The input/output widths are determined automatically by the compiler, via type inference.)
+  The type of the internal structure (i.e. - hidden layers) is existentially hidden, outside
+  the API, which offers the following benefits:
+
+  - Client generated networks of different internal structure may be stored in a common list
+  (or, other Functorial data structure).
+
+  - The exact structure of the network may be specified at run time, via: user input, file I/O, etc.,
+  while still providing GHC enforced type safety, at compile time.
+
+  - Complex networks with long training times may be stored, after being trained, so that they
+  may be recalled and used again, at a later date/time, without having to re-train them.
+
 license:             BSD3
 license-file:        LICENSE
 author:              David Banas