Fix titles and navigation

docs/knowledge_base/tips/big_data_models/index.md

@@ -16,7 +16,7 @@ enables effortless management of large datasets
 This article focuses on the seamless integration of Dask (for handling out-of-core data)
  with Taipy, a Python library used for pipeline orchestration and scenario management.
 
-# 1. Sample Application
+# Sample Application
 Integrating Dask and Taipy is demonstrated best with an example. In this article, we'll
 consider a Taipy data workflow with four tasks:
 
@@ -49,7 +49,7 @@ config.toml  # (Optional) Taipy configuration in TOML made using Taipy Studio
 ```
 <hr/>
 
-# 2. Introducing Taipy — A Comprehensive Solution
+# Taipy Solution
 
 Taipy is more than just another orchestration tool. Specially designed for ML engineers,
 data scientists, and Python developers, Taipy brings several essential and straightforward features.
@@ -86,7 +86,7 @@ data sources that can be used interchangeably, resulting in a cleaner, more main
 
 <hr/>
 
-# 3. Introducing Dask
+# Introducing Dask
 
 Dask is a popular Python package for distributed computing. The Dask API implements the
 familiar Pandas, Numpy, and Scikit-learn APIs — which makes learning and using Dask much
@@ -98,7 +98,7 @@ by the Dask team.
 
 <hr/>
 
-# 4. Application: Customer Analysis (algos/algo.py)
+# Application: Customer Analysis
 
 ![Taipy Graph](images/graph.png){width=80% style="margin:auto;display:block"}
 
@@ -241,7 +241,7 @@ def high_value_cust_summary_statistics(df: pd.DataFrame, segment_analysis: pd.Da
     return result_df
 ```
 
-## Task 1 — Data Preprocessing and Customer Scoring (*preprocess_and_score()*)
+<h3>Task 1 — Data Preprocessing and Customer Scoring (*preprocess_and_score()*)</h3>
 
 This is the first step in your pipeline and perhaps the most crucial. It reads a large
 dataset using **Dask**, designed for larger-than-memory computation. It then calculates a
@@ -251,22 +251,24 @@ dataset using **Dask**, designed for larger-than-memory computation. It then cal
 After reading and processing the dataset with Dask, this task will output a Pandas
 DataFrame for further use in the remaining three tasks.
 
-## Task 2 — Feature Engineering and Segmentation (*featurization_and_segmentation()*)
+<h3>
+Task 2 — Feature Engineering and Segmentation (*featurization_and_segmentation()*)
+</h3>
 
 This task takes the scored DataFrame and adds new features, such as high spending indicators It
 also segments the customers based on their scores.
 
-## Task 3 — Segment Analysis (*segment_analysis()*)
+<h3>Task 3 — Segment Analysis (*segment_analysis()*)</h3>
 
 This task takes the segmented DataFrame and performs a group-wise analysis based on the
 customer segments to calculate various metrics.
 
-## Task 4 — Summary Statistics for Customers (*high_value_cust_summary_statistics()*)
+<h3>Task 4 — Summary Statistics for Customers (*high_value_cust_summary_statistics()*)</h3>
 
 This task performs an in-depth analysis of the high-value customer segment and returns
 summary statistics.
 
-# 5. Modeling the Workflow in Taipy (config.py)
+# Modeling the Workflow
 
 ![Taipy DAG](images/dag.png){width=80% style="margin:auto;display:block;border: 4px solid rgb(210,210,210);border-radius:7px" }
 
@@ -363,7 +365,7 @@ You can read more about configuring Scenarios, Tasks, and Data Nodes in the
 [documentation here](../../../manuals/core/config/index.md).
 
 
-# 6. Scenario Creation and Execution
+# Scenario Creation and Execution
 
 Executing a Taipy scenario involves:
 
@@ -393,7 +395,7 @@ if __name__ == "__main__":
 One of Taipy's most practical features is its ability to skip a task execution if its output is
 already computed. Let's explore this with some scenarios:
 
-### Changing Payment Threshold
+**Changing Payment Threshold**
 
 ```python
 # Changing Payment Threshold to 1600
@@ -407,7 +409,7 @@ scenario_1.submit()
 affects Task 2. In this case, we see more than a 50% reduction in execution time by running
 your pipeline with Taipy.
 
-### Changing Metric for Segment Analysis
+**Changing Metric for Segment Analysis**
 
 ```python
 # Changing metric to median
@@ -421,7 +423,7 @@ scenario_1.submit()
 and Task 2.
 
 
-### Changing Summary Statistic Type
+**Changing Summary Statistic Type**
 ```python
 # Changing summary_statistic_type to max
 scenario_1.summary_statistic_type.write("max")
@@ -439,7 +441,7 @@ incredibly useful when dealing with large datasets.
 <hr/>
 
 
-# 7. Taipy Studio
+# Taipy Studio
 
 If you have VS Code, you may use [Taipy Studio](../../../manuals/studio/config/index.md)
 to build the Taipy *config.toml* configuration file in place of defining the

docs/knowledge_base/tips/databricks/index.md

@@ -14,7 +14,7 @@ versions of a business problem. In this article, we'll look into the integration
 Databricks jobs with Taipy scenarios, showcasing how this can elevate your data 
 processing capabilities.
 
-![Databricks](databricks.png){width=100%}
+![Databricks](databricks.png){width=50% style="margin:auto;display:block;"}
 
 # Scenarios and Databricks Integration
 
@@ -38,7 +38,7 @@ create the notebook.
 - **Define Notebook Details:** Enter a name for your notebook, choose the language
 (e.g., Python, Scala, or SQL), and select the cluster you want to use.
 
-** 2 - Define Databricks Job Logic**
+**2 - Define Databricks Job Logic**
 
 - **Create the Cluster**: Go to the Compute section to create a cluster with your
 packages required by your code. You would also need to install `dbutils` to be able to