LIN-716 Reorganize RTD contents (LineaLabs#856)

* Create <Concepts> section

* Create <Tutorials> section

* Create <Guides> section

* Move intro content to the landing page

* Reorganize contribution-related contents

* Create help page

* Reorganize configuration-related contents

* Reorganize contents relating to code cleanup

* Make configuration section multipage upfront

* Make basics page for code cleanup guide section

* Make a guide content category for using artifacts

* Absorb code-cleanup contents into <Using Artifacts> category

* Make <Troubleshooting> an independent guide content category

* Remove outdated file

* Use more moderate message

* Reorganize pipeline-related contents

* Simplify document structure of configuration-related contents

* Declutter and streamline tutorial notebooks (changes in docs/ only here, for demo purpose)

* Fix rendering of <Open in Colab> button

* Create a quickstart tutorial notebook

* Add docs feedback cards

* Instrument docs to open external links in a new tab

* Redesign card for Slack support

* Fix warnings

* Remove docs feedback cards (DMX suggested outgoing links would not help)

* Synchronize tutorial notebooks across different documentation surfaces

* Refresh demos folder and update docs

* Add missed tutorial notebook

* Add missed image update

* Update README for tutorial notebooks

* Refresh demos folder and update docs

* Add back feedback cards by directly embedding Google Forms

* Update GUIDES contents to be more user-intent-driven

* Store common image assets in S3

* Delete outdated notebook remnant

* Better naming convention as suggested by SSS

* Restore regexing-out of the NBVAL_SKIP and NBVAL_IGNORE_OUTPUT comments

* Refresh demos folder and update docs

* Route contributors to project documentation

* Update links to RTD

Co-authored-by: Humble bot servant <infra@linea.ai>

.colab/README.md

@@ -1,15 +1,13 @@
-# LineaPy Use Cases
+# LineaPy Tutorials
 
-## `predict_house_price`
+## `00_lineapy_quickstart`
 
-This use case illustrates how LineaPy can facilitate an end-to-end data science workflow for housing price prediction.
-The notebook comes in 3 main sections:
+This tutorial gives you a quick tour of core functionalities of LineaPy. If you are new to LineaPy, start here!
 
-1. ***Exploratory Data Analysis and Feature Engineering.*** Using various statistics and visualizations, we explore the given data
-to create useful features. We use LineaPy to store the transformed data as an artifact, which allows us to automatically refactor and clean up the code.
+## `01_using_artifacts`
 
-2. ***Training a Model.*** Using the transformed data, we train a model that can predict housing prices. We then store
-the trained model as an artifact.
+This tutorial uses simple examples to demonstrate how to work with LineaPy artifacts.
 
-3. ***Building an End-to-End Pipeline.*** Using artifacts saved in this session, we quickly build an end-to-end
-pipeline that combines data preprocessing and model training, moving closer to production.
+## `02_pipeline_building`
+
+Data science workflows revolve around building and refining pipelines, but it is often manual and time-consuming work. Having the complete development process stored in artifacts, LineaPy can automate pipeline building, accelerating transition from development to production. This tutorial demonstrates how this can be done.

.colab/clean_up_a_messy_notebook/clean_up_a_messy_notebook.ipynb

@@ -73,7 +73,7 @@
             "source": [
                 "## Scenario\n",
                 "\n",
-                "As a data scientist using the Jupyter notebook day-to-day, we embrace non-linear workflows, i.e., jumping around between cells, deleting cells, editing cells, and executing the same cell multiple times until we think we have some good results or \"[artifacts](https://docs.lineapy.org/en/latest/fundamentals/concepts.html#artifact)\" (e.g., tables, models, charts). Finally, we share our results and notebook with our colleagues/supervisors/stakeholders to declare victory."
+                "As a data scientist using the Jupyter notebook day-to-day, we embrace non-linear workflows, i.e., jumping around between cells, deleting cells, editing cells, and executing the same cell multiple times until we think we have some good results or \"[artifacts](https://docs.lineapy.org/en/main/concepts/artifact.html#artifact)\" (e.g., tables, models, charts). Finally, we share our results and notebook with our colleagues/supervisors/stakeholders to declare victory."
             ]
         },
         {
@@ -139,7 +139,7 @@
                 "\n",
                 "In sum, the demo will show that LineaPy can help data scientists and ML engineers save time on notebook cleanup and instead focus more on generating insights.\n",
                 "\n",
-                "We strongly encourage you to try this notebook on your own and check out the official [documentation](https://docs.lineapy.org/en/latest/index.html) to learn more use cases of LineaPy."
+                "We strongly encourage you to try this notebook on your own and check out the official [documentation](https://docs.lineapy.org/en/main/index.html) to learn more use cases of LineaPy."
             ]
         },
         {
@@ -1001,7 +1001,7 @@
             "source": [
                 "This demo has shown that LineaPy can help data scientists and ML engineers save time on manual notebook cleanup, which is often a major bottleneck in data science workflows. Hence, they can focus on more valuable activities such as generating insights from the data.\n",
                 "\n",
-                "To learn more about LineaPy, check out the project [documentation](https://docs.lineapy.org/en/latest/index.html)."
+                "To learn more about LineaPy, check out the project [documentation](https://docs.lineapy.org/en/main/index.html)."
             ]
         }
     ],