Update links for new docs structure (towhee-io#1215)

Signed-off-by: Frank Liu <[email protected]>

docs/01-Overview.md

@@ -11,23 +11,23 @@ Towhee is a framework that provides [ETL](https://databricks.com/glossary/extrac
 
 Unstructured data refers to data that cannot be stored in a tabular or key-value format. Nearly all human-generated data (images, video, text, etc...) is unstructured - some market analysts estimate that over 80% of data generated by 2024 will be unstructured data. Towhee is the first open-source project that's meant to process a variety of unstructured data using ETL pipelines.
 
-To accomplish this, we built Towhee atop popular machine learning and unstructured data processing libraries, i.e. `torch`, `timm`, `transformers`, etc. Models or functions from different libraries are wrapped as standard Towhee operators, and can be freely integrated into application-oriented pipelines using a [Pythonic API](/02-Getting%20Started/03-data-collection.md). To ensure user-friendliness, pre-built pipelines can also be called in just a single line of code, without the need to understand the underlying models or modules used to build them.
+To accomplish this, we built Towhee atop popular machine learning and unstructured data processing libraries, i.e. `torch`, `timm`, `transformers`, etc. Models or functions from different libraries are wrapped as standard Towhee operators, and can be freely integrated into application-oriented pipelines using a [Pythonic API](/03-User%20Guides/01-DataCollection/01-data-collection.md). To ensure user-friendliness, pre-built pipelines can also be called in just a single line of code, without the need to understand the underlying models or modules used to build them.
 
 For more information, take a look at our [quick start](/02-Getting%20Started/01-quick-start.mdx) page.
 
 ## Problems Towhee solves
 
 - **Modern ML applications require far more than a single neural network.** Running a modern ML application in production requires a combination of online pre-processing, data transformation, the models themselves, and other ML-related tools. Building an application that recognizes objects within a video, for example, involves decompression, key-frame extraction, image deduplication, object detection, etc. This necessitates a platform that offers a fast and robust method for developing end-to-end application pipelines that use ML models in addition to supporting data parallelism and resource management.
 
-  Towhee solves this problem by reintroducing the concept of `Pipeline` as being _application-centric_ instead of _model-centric_. Where model-centric pipelines are composed of a single model followed by auxiliary code, application-centric pipelines treat every single data processing step as a first-class citizen. Towhee also exposes a [Pythonic API](/02-Getting%20Started/03-data-collection.md) for developing more complex applications in just a couple lines of code.
+  Towhee solves this problem by reintroducing the concept of `Pipeline` as being _application-centric_ instead of _model-centric_. Where model-centric pipelines are composed of a single model followed by auxiliary code, application-centric pipelines treat every single data processing step as a first-class citizen. Towhee also exposes a [Pythonic API](/03-User%20Guides/01-DataCollection/01-data-collection.md) for developing more complex applications in just a couple lines of code.
 
 - **Too many model implementations exist without any interface standard.** Machine learning models (NN-based and traditional) are ubiquitous. Different implementations of machine learning models require different auxiliary code to support testing and fine-tuning, making model evaluation and productionization a tedious task.
 
   Towhee solves this by providing a universal `Operator` wrapper for dataset loading, basic data transformations, ML models, and other miscellaneous scripts. Operators have a pre-defined API and glue logic to make Towhee work with a number of machine learning and data processing libraries. Operators can be chained together in a DAG to form entire ML applications.
 
 - **ETL pipelines for unstructured data are nearly nonexistent.** ETL, short for _extract, transform, and load_, is a framework used by data scientists, ML application developers, and other engineers to extract data from various sources, transform the data into a format that can be understood by computers, and load the data into downstream platforms for recommendation, analytics, and other business intelligence tasks.
 
-  Towhee solves this by providing an open-source vision for ETL in the era of unstructured data. We provide: 1) over 300 pre-built pipelines across a multitude of different data transformation tasks (including but not limited to image embedding, audio embedding, text summarization), and 2) a way to build pipelines of arbitrary complexity through an intuitive Python API called [`DataCollection`](/02-Getting%20Started/03-data-collection.md).
+  Towhee solves this by providing an open-source vision for ETL in the era of unstructured data. We provide: 1) over 300 pre-built pipelines across a multitude of different data transformation tasks (including but not limited to image embedding, audio embedding, text summarization), and 2) a way to build pipelines of arbitrary complexity through an intuitive Python API called [`DataCollection`](/03-User%20Guides/01-DataCollection/01-data-collection.md).
 
 ## Design philosophy
 
@@ -46,9 +46,9 @@ For more information, take a look at our [quick start](/02-Getting%20Started/01-
 
 ### Tutorials:
 
-- [Reverse image search](/03-Tutorials/01-reverse-image-search.md): search for similar or related images.
-- [Image deduplication](/03-Tutorials/03-image-deduplication.md): detect and remove identical or near-identical photos.
-- [Music recognition](/03-Tutorials/02-music-recognition-system.md): music identification with full-length song or a snippet.
+- [Reverse image search](/05-Tutorials/02-Computer%20Vision/01-reverse-image-search.md): search for similar or related images.
+- [Image deduplication](/05-Tutorials/02-Computer%20Vision/02-image-deduplication.md): detect and remove identical or near-identical photos.
+- [Music recognition](/05-Tutorials/03-Audio/01-music-recognition-system.md): music identification with full-length song or a snippet.
 
 ### Community:
 

docs/03-User Guides/03-Built-in Pipelines/audio-embedding.md

@@ -4,7 +4,7 @@ Audio is defined as any human-hearable sound; audio embedding is the process of
 
 ## Popular scenarios
 
-- [Identify music with an audio snippet](/03-Tutorials/02-music-recognition-system.md)
+- [Identify music with an audio snippet](/05-Tutorials/03-Audio/01-music-recognition-system.md)
 - Recognize audio events or scenes
 - Tag music for genres, artists, emotion
 - Music copyright infringement

docs/03-User Guides/03-Built-in Pipelines/image-embedding.md

@@ -4,8 +4,8 @@ Image embedding pipelines are used for reduction the dimensionality of the input
 
 ## Popular Scenarios
 
-- [Reverse image search](/03-Tutorials/01-reverse-image-search.md)
-- [Image deduplication](/03-Tutorials/03-image-deduplication.md)
+- [Reverse image search](/05-Tutorials/02-Computer%20Vision/01-reverse-image-search.md)
+- [Image deduplication](/05-Tutorials/02-Computer%20Vision/02-image-deduplication.md)
 - Copyright infringement detection
 - Item tagging
 - Celebrity tagging

docs/06-Release Notes/040-release-notes.md → docs/06-Release Notes/release-notes-040.md

@@ -1,3 +1,5 @@
+# Towhee v0.4.0 Release Notes
+
 #### Highlights
 - The Towhee website has a new look and feel! The new Towhee website includes several important docs, including in-depth Towhee tutorials, pipeline and operator summaries, development and contributing guides, and more. See https://docs.towhee.io. If you have any feedback for the website design or encounter any bugs, please open an issue through Github.
 - Towhee now offers pre-built embedding pipelines that use transformer-based models: SwinTransformer and ViT.

docs/06-Release Notes/050-release-notes.md → docs/06-Release Notes/release-notes-050.md

@@ -1,4 +1,4 @@
-# v0.5.0 Release Notes
+# Towhee v0.5.0 Release Notes
 
 #### Highlights
 - training/fine-tuning is supported! For the cases when an operator is a wrapper of neural network model,  users can train or fine-tune the model via `train()` method of the operator.

docs/06-Release Notes/060-release-notes.md → docs/06-Release Notes/release-notes-060.md

@@ -1,4 +1,4 @@
-# v0.6.0 Release Notes
+# Towhee v0.6.0 Release Notes
 
 #### Highlights