docs: add a Getting Started Section

[TomasPegado]

This pull request looks to enhances the documentation by introducing a "Getting Started" section to help new users quickly understand and begin working with the Nx library in Elixir.

Key Additions:

• Introduction Page – Provides an overview of Nx, its purpose, and key features.
• Quickstart Guide – Offers instructions on how to run basic operations, making onboarding smoother for new users.

[polvalente]

@josevalim This is the start of our revamped docs.

We're taking the current getting started guide and both splitting it and getting into more detail.
I'm thinking we should merge these onto a new-docs branch and only merge that onto main after the getting started is fully done.

WDYT?

[josevalim]

I think it is fine to push to main directly, given the plan is for continuous work on it, right?

[polvalente]

We'll make it so that this PR is merged when it fully replaces the previous introduction to nx guide.

[polvalente]

Suggested change

	## Using Mix in a standardElixir Project
	## Using Nx in a Standard Elixir Project

[polvalente]

Suggested change

[polvalente]

Suggested change

	{:nx, github: "elixir-nx/nx", branch: "main"}
	{:nx, github: "elixir-nx/nx", branch: "main", sparse: "nx"}

[polvalente]

Suggested change

	IO.inspect(tensor)
	IO.inspect(tensor)

[polvalente]

Suggested change

	If you don’t have a Mix project and just want to run a standalone script, use Mix.install/1 to dynamically fetch and install Nx.
	If you don’t have a Mix project and just want to run a standalone script, use `Mix.install/1` to dynamically fetch and install Nx.

[polvalente]

Suggested change

[polvalente]

Suggested change

	{:nx, github: "elixir-nx/nx", branch: "main"}
	{:nx, github: "elixir-nx/nx", branch: "main", sparse: "nx"}

[polvalente]

Suggested change

	Best for: Running Nx on GPUs or TPUs using Google’s XLA compiler.
	## Installing Nx with Torchx for PyTorch Acceleration
	To run Nx operations on PyTorch’s backend (LibTorch):
	1. Modify mix.exs:
	```elixir
	defp deps do
	[
	{:nx, "~> 0.5"},
	{:torchx, "~> 0.5"} # PyTorch Backend
	]
	end
	```

Let's not mention Torchx. We could maybe reference EMLX, but it's not even released yet, so let's leave this for later.

[polvalente]

Suggested change

	2. Fetch dependencies:
	```sh
	mix deps.get
	```
	3. Run with EXLA enabled:
	```elixir
	Torchx.set_preferred_backend()
	```
	Best for: Deep learning applications with PyTorch acceleration.
	## Installing Nx with OpenBLAS for CPU Optimization
	To optimize CPU performance with OpenBLAS:
	1. Install OpenBLAS (libopenblas):
	- Ubuntu/Debian:
	```sh
	sudo apt install libopenblas-dev
	```
	- MacOS (using Homebrew):
	```sh
	brew install openblas
	```
	2. Modify mix.exs:
	```elixir
	defp deps do
	[
	{:nx, "~> 0.5"},
	{:openblas, "~> 0.5"} # CPU-optimized BLAS backend
	]
	end
	```
	3. Fetch dependencies:
	```sh
	mix deps.get
	```
	Best for: Optimizing CPU-based tensor computations.

[polvalente]

Let's add an example that does shape validation and manipulation, such as adding a new axis and changing things around

[TomasPegado]

On it!

[polvalente]

Congrats! The new documentation covers quite a bit more surface area, and will probably allow for more growth in the future!