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JZakraoui · Sep 21, 2021 · bd5eed8 · bd5eed8
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diff --git a/call_for_contributions.md b/call_for_contributions.md
@@ -4,6 +4,13 @@ This is a constantly-updated list of code examples that we're currently interest
 
 --
 
+## Structured data examples featuring Keras Preprocessing Layers (KPL)
+
+E.g. feature hashing, feature indexing with handling of missing values,
+mixing numerical, categorical, and text features, doing feature engineering with KPL, etc.
+
+--
+
 ## Transformer model for MIDI music generation
 
 [Reference TF/Keras implementation](https://github.com/jason9693/MusicTransformer-tensorflow2.0)
@@ -79,3 +86,9 @@ The dataset should have at least 50k samples and there should be at least a doze
 
 --
 
+## Customer lifetime value prediction
+
+--
+
+
+
diff --git a/examples/vision/ipynb/zero_dce.ipynb b/examples/vision/ipynb/zero_dce.ipynb
@@ -35,7 +35,7 @@
     "curve estimation is inspired by curves adjustment used in photo editing software such as\n",
     "Adobe Photoshop where users can adjust points throughout an image\u2019s tonal range.\n",
     "\n",
-    "Zero-DCE is appealing because of its relaxed assumptions with regard tp reference images:\n",
+    "Zero-DCE is appealing because of its relaxed assumptions with regard to reference images:\n",
     "it does not require any input/output image pairs during training.\n",
     "This is achieved through a set of carefully formulated non-reference loss functions,\n",
     "which implicitly measure the enhancement quality and guide the training of the network.\n",

diff --git a/examples/vision/md/zero_dce.md b/examples/vision/md/zero_dce.md
@@ -1,4 +1,3 @@
-
 # Zero-DCE for low-light image enhancement
 
 **Author:** [Soumik Rakshit](http://github.com/soumik12345)<br>
@@ -27,7 +26,7 @@ the range of the enhanced image and preserves the contrast of neighboring pixels
 curve estimation is inspired by curves adjustment used in photo editing software such as
 Adobe Photoshop where users can adjust points throughout an image’s tonal range.
 
-Zero-DCE is appealing because of its relaxed assumptions with regard tp reference images:
+Zero-DCE is appealing because of its relaxed assumptions with regard to reference images:
 it does not require any input/output image pairs during training.
 This is achieved through a set of carefully formulated non-reference loss functions,
 which implicitly measure the enhancement quality and guide the training of the network.

diff --git a/examples/vision/zero_dce.py b/examples/vision/zero_dce.py
@@ -21,7 +21,7 @@
 curve estimation is inspired by curves adjustment used in photo editing software such as
 Adobe Photoshop where users can adjust points throughout an image’s tonal range.
 
-Zero-DCE is appealing because of its relaxed assumptions with regard tp reference images:
+Zero-DCE is appealing because of its relaxed assumptions with regard to reference images:
 it does not require any input/output image pairs during training.
 This is achieved through a set of carefully formulated non-reference loss functions,
 which implicitly measure the enhancement quality and guide the training of the network.