[docs] Fixes markdown headers (#2812)

This fixes the markdown headers to be h1 so they render correctly in docs.
deepjavalibrary · Oct 18, 2023 · 298ea1f · 298ea1f
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diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
@@ -1,4 +1,5 @@
-## Code of Conduct
+# Code of Conduct
+
 This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
 For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
 [email protected] with any additional questions or comments.
diff --git a/docker/README.md b/docker/README.md
@@ -1,10 +1,12 @@
 # Docker Resources
+
 DJL provides docker files that you can use to setup containers with the appropriate environment for certain platforms.
 
 We recommend setting up a docker container with the provided Dockerfile when developing for the following
 platforms and/or engines.
 
 ## Windows
+
 You can use the [docker file](https://github.com/deepjavalibrary/djl/blob/master/docker/windows/Dockerfile) provided by us.
 Please note that this docker will only work with Windows server 2019 by default. If you want it to work with other
 versions of Windows, you need to pass the version as an argument as follows:
@@ -14,6 +16,7 @@ docker build --build-arg version=<YOUR_VERSION>
 ```
 
 ## TensorRT
+
 You can use the [docker file](https://github.com/deepjavalibrary/djl/blob/master/docker/tensorrt/Dockerfile) provided by us.
 This docker file is a modification of the one provided by NVIDIA in
 [TensorRT](https://github.com/NVIDIA/TensorRT/blob/8.4.1/docker/ubuntu-18.04.Dockerfile) to include JDK11. 

diff --git a/docs/development/example_dataset.md b/docs/development/example_dataset.md
@@ -1,4 +1,4 @@
-## Example CSV Dataset
+# Custom CSV Dataset Example
 
 If the provided Datasets don't meet your requirements, you can also easily extend our dataset to create your own customized dataset.
 

diff --git a/docs/development/external_libraries.md b/docs/development/external_libraries.md
@@ -1,5 +1,4 @@
-
-## DJL external dependencies
+# DJL external dependencies
 
 This document contains external libraries that DJL depends on and their versions.
 

diff --git a/docs/development/profiler.md b/docs/development/profiler.md
@@ -1,4 +1,4 @@
-## Profiler (Experimental)
+# Engine Profiler Support
 
 Currently, DJL supports experimental profilers for developers that 
 investigate the performance of operator execution as well as memory consumption.

diff --git a/docs/mxnet/how_to_convert_your_model_to_symbol.md b/docs/mxnet/how_to_convert_your_model_to_symbol.md
@@ -1,4 +1,4 @@
-## How to convert your Gluon model to an MXNet Symbol
+# How to convert your Gluon model to an MXNet Symbol
 
 DJL currently supports symbolic model loading from MXNet.
 A gluon [HybridBlock](https://mxnet.apache.org/api/python/docs/api/gluon/hybrid_block.html) can be converted into a symbol for loading by doing as follows:

diff --git a/docs/pytorch/how_to_convert_your_model_to_torchscript.md b/docs/pytorch/how_to_convert_your_model_to_torchscript.md
@@ -1,4 +1,4 @@
-## How to convert your PyTorch model to TorchScript
+# How to convert your PyTorch model to TorchScript
 
 There are two ways to convert your model to TorchScript: tracing and scripting.
 We will only demonstrate the first one, tracing, but you can find information about scripting from the PyTorch documentation.

diff --git a/docs/pytorch/pytorch-djl-ndarray-cheatsheet.md b/docs/pytorch/pytorch-djl-ndarray-cheatsheet.md
@@ -1,4 +1,4 @@
-## PyTorch NDArray operators
+# PyTorch NDArray operators
 
 In the following examples, we assume
 

diff --git a/examples/docs/stable_diffusion.md b/examples/docs/stable_diffusion.md
@@ -1,4 +1,4 @@
-## Stable Diffusion in DJL
+# Stable Diffusion in DJL
 
 [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) is an open-source model
 developed by Stability.ai. It aimed to produce images (artwork, pictures, etc.) based on

diff --git a/extensions/timeseries/docs/forecast_with_M5_data.md b/extensions/timeseries/docs/forecast_with_M5_data.md
@@ -1,5 +1,7 @@
 # Forecast the future in a timeseries data with Deep Java Library (DJL)
+
 ## -- Demonstration on M5forecasting and airpassenger datasests
+
 Junyuan Zhang, Kexin Feng
 
 Time series data are commonly seen in the world. They can contain valued information that helps forecast for the future, monitor the status of a procedure and feedforward a control. Generic applications includes the following: sales forecasting, stock market analysis, yield projections, process and quality control, and many many more. See [link1](https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc41.htm) and [link2](https://www.influxdata.com/time-series-forecasting-methods/#:~:text=Time%20series%20forecasting%20means%20to,on%20what%20has%20already%20happened) for further examples of timeseries data.