diff --git a/notebooks/c07_Recurrent_Neural_Networks/06_diagram_ignore.py b/notebooks/c07_Recurrent_Neural_Networks/06_diagram_ignore.py
deleted file mode 100644
index ed07a84..0000000
--- a/notebooks/c07_Recurrent_Neural_Networks/06_diagram_ignore.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# ---
-# jupyter:
-#   jupytext:
-#     formats: ipynb,py
-#     text_representation:
-#       extension: .py
-#       format_name: light
-#       format_version: '1.5'
-#       jupytext_version: 1.6.0
-#   kernelspec:
-#     display_name: deep_ml_curriculum
-#     language: python
-#     name: deep_ml_curriculum
-# ---
-
-# +
-
-
-import torch
-from torch import nn, optim
-from torch import functional as F
-from torch.autograd import Variable
-import numpy as np
-import matplotlib.pyplot as plt
-from tqdm.auto import tqdm
-import xarray as xr
-
-# + [markdown] colab_type="text" id="kzlqXAj4EIBN"
-# In this example we are going to look at well logs which are sequential data as well.
-
-# + colab={"base_uri": "https://localhost:8080/", "height": 255} colab_type="code" id="uNl846nE-jjq" outputId="de7b4197-6a3f-4e88-e07e-2463adba90d0"
-import pandas as pd
-import xarray as xr
-xf = xr.open_zarr("../../data/processed/geolink_norge_dataset/geolink_norge_well_logs.zarr")
-xf = xf.where(xf['Well'].str.startswith('30')).dropna(dim='Well', how='all')
-df = xf.to_dataframe().swaplevel()
-df['LITHOLOGY_GEOLINK'] = df['LITHOLOGY_GEOLINK'].astype('category')
-df['Well'] = df.index.get_level_values(0).astype('category')
-df['DEPT'] = df.index.get_level_values(1)
-feature_cols = ['CALI', 'DTC', 'GR', 'RDEP', 'RHOB',
-       'RMED', 'xc', 'yc', 'DEPT']
-df = df.dropna(how='any', subset=feature_cols+['LITHOLOGY_GEOLINK'])
-df = df.sort_index()
-df
-# -
-
-# %reload_ext autoreload
-# %autoreload 2
-
-# +
-# DEBUG plot
-from deep_ml_curriculum.visualization.well_log import plot_facies, plot_well, plot_well_pred
-well_name = "30_4-1"
-a=5200
-b=5380
-logs = df.loc[well_name].loc[a:b].copy()
-facies = logs['LITHOLOGY_GEOLINK'].astype('category').values
-
-facies = facies.add_categories('[Hidden]')
-facies_true = facies.copy()
-facies[-100:] = '[Hidden]'
-
-f, ax= plot_well_pred(
-    well_name, 
-    logs, 
-    facies_true=facies_true,
-    facies_pred=facies,
-)
-ax[-2].set_xlabel("Facies\n(model input)")
-plt.savefig(dpi=300, fname='plot.png')
-
-# +
-# plt.yticks?
-# -
-
-# context length of 150.0 m or 1000 intervals
-# model can see human labels up to 15.0m above. Or 100 intervals
diff --git a/notebooks/c07_Recurrent_Neural_Networks/Recurrent_Neural_Networks.ipynb b/notebooks/c07_Recurrent_Neural_Networks/RNN_Depthseries.ipynb
similarity index 100%
rename from notebooks/c07_Recurrent_Neural_Networks/Recurrent_Neural_Networks.ipynb
rename to notebooks/c07_Recurrent_Neural_Networks/RNN_Depthseries.ipynb
diff --git a/notebooks/c07_Recurrent_Neural_Networks/Recurrent_Neural_Networks.py b/notebooks/c07_Recurrent_Neural_Networks/RNN_Depthseries.py
similarity index 100%
rename from notebooks/c07_Recurrent_Neural_Networks/Recurrent_Neural_Networks.py
rename to notebooks/c07_Recurrent_Neural_Networks/RNN_Depthseries.py
diff --git a/notebooks/c07_Recurrent_Neural_Networks/03_mike-seq2seq_timeseries.ipynb b/notebooks/c07_Recurrent_Neural_Networks/RNN_Timeseries_Seq2Seq.ipynb
similarity index 100%
rename from notebooks/c07_Recurrent_Neural_Networks/03_mike-seq2seq_timeseries.ipynb
rename to notebooks/c07_Recurrent_Neural_Networks/RNN_Timeseries_Seq2Seq.ipynb
diff --git a/notebooks/c07_Recurrent_Neural_Networks/03_mike-seq2seq_timeseries.py b/notebooks/c07_Recurrent_Neural_Networks/RNN_Timeseries_Seq2Seq.py
similarity index 100%
rename from notebooks/c07_Recurrent_Neural_Networks/03_mike-seq2seq_timeseries.py
rename to notebooks/c07_Recurrent_Neural_Networks/RNN_Timeseries_Seq2Seq.py