From 7d10ac1e91810b75a7e38b5c7586534fae265114 Mon Sep 17 00:00:00 2001
From: J1an5 <1592050303@qq.com>
Date: Tue, 14 Jan 2025 13:57:00 +0800
Subject: [PATCH] Update docs title.

---
 docs/source/tutorial/convolutions.rst | 2 +-
 docs/source/tutorial/gnns.rst         | 2 +-
 docs/source/tutorial/rtls.rst         | 2 +-
 docs/source/tutorial/tnns.rst         | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/source/tutorial/convolutions.rst b/docs/source/tutorial/convolutions.rst
index 7c531e03..9ca80459 100644
--- a/docs/source/tutorial/convolutions.rst
+++ b/docs/source/tutorial/convolutions.rst
@@ -1,7 +1,7 @@
 Understanding Convolution
 ===============
 
-What is a convolution?
+What is a Convolution?
 ----------------
 In machine learning, convolution generally involves combining an input signal with a filter to produce an output signal. Specifically, for image signals, convolution refers to aggregating nearby pixels around the central pixel. For graph signals, it involves aggregating information from connected nodes around the central node. For table signals, it refers to aggregating entries from different columns within each row. Therefore, designing an effective convolution operation is a key challenge in deep learning methods.
 
diff --git a/docs/source/tutorial/gnns.rst b/docs/source/tutorial/gnns.rst
index ef0ec590..f7e58142 100644
--- a/docs/source/tutorial/gnns.rst
+++ b/docs/source/tutorial/gnns.rst
@@ -1,7 +1,7 @@
 Design of GNNs
 ===============
 
-What is GNN?
+What is a GNN?
 ----------------
 In machine learning, **Graph Neural Networks (GNNs)** are a class of neural networks specifically designed to process graph-structured data. In a GNN, the input is represented as a graph, where nodes (vertices) correspond to entities and edges represent the relationships or interactions between these entities. A typical GNN architecture consists of an initial Transform followed by multiple Convolution layers, as detailed in *Understanding Transforms* and *Understanding Convolutions*.
 
diff --git a/docs/source/tutorial/rtls.rst b/docs/source/tutorial/rtls.rst
index 6ff4feca..661738cc 100644
--- a/docs/source/tutorial/rtls.rst
+++ b/docs/source/tutorial/rtls.rst
@@ -1,7 +1,7 @@
 Design of RTLs
 ==============
 
-What is RTL?
+What is a RTL?
 ----------------
 In machine learning, **Relational Table Learnings (RTLs)** typically refers to the learning of relational table data, which consists of multiple interconnected tables with significant heterogeneity. In an RTL, the input comprises multiple table signals that are interrelated.  A typical RTL architecture consists of one or more Transforms followed by multiple Convolution layers, as detailed in **Understanding Transforms** and **Understanding Convolutions**.
 
diff --git a/docs/source/tutorial/tnns.rst b/docs/source/tutorial/tnns.rst
index 564c5a45..2f1de544 100644
--- a/docs/source/tutorial/tnns.rst
+++ b/docs/source/tutorial/tnns.rst
@@ -1,6 +1,6 @@
 Design of TNNs
 ===============
-What is TNN?
+What is a TNN?
 ----------------
 In machine learning, **Table/Tabular Neural Networks (TNNs)** are recently emerging neural networks specifically designed to process tabular data. In a TNN, the input is structured tabular data, usually organized in rows and columns. A typical TNN architecture consists of an initial Transform followed by multiple Convolution layers, as detailed in *Understanding Transforms* and *Understanding Convolutions*.