factor out nn_train.py into a separate python project to make it more manageable, add plotting and offline strength fit

1 files changed, 1 insertions, 1 deletions

diff --git a/README.md b/README.md
index 762c366..f20fba2 100644
--- a/README.md
+++ b/README.md
@@ -22,7 +22,7 @@ Download and extract a recent version of buildroot into the working directory. E
 
 Standard adversarial tree search (α-β negamax) with iterative deepening, using a neural network evaluation function for leaves and also a transposition table keyed on the Zobrist hash.
 
-The architecture of the neural network is a standard, two-layer dense network which is trained exclusively through self-play using td-leaf (see `src/nn_train.py`). As input the network is fed the top layer of the board only as well as `flats used/flats remaining` fractions for both players and a single float indicating the parity of the board.
+The architecture of the neural network is a standard, two-layer dense network which is trained exclusively through self-play using td-leaf (see the `train/` uv project: `cd train && uv run nn-train`). As input the network is fed the top layer of the board only as well as `flats used/flats remaining` fractions for both players and a single float indicating the parity of the board.
 
 ## License