remove old quick start guide (unused)

docs/source/new-project.rst

@@ -1,72 +0,0 @@
-Quick Start
-===========
-| To start a new project define two files, a LightningModule and a Trainer file.
-| To illustrate the power of Lightning and its simplicity, here's an example of a typical research flow.
-
-Case 1: BERT
-------------
-
-| Let's say you're working on something like BERT but want to try different ways of training or even different networks.
-| You would define a single LightningModule and use flags to switch between your different ideas.
-
-.. code-block:: python
-
-    class BERT(pl.LightningModule):
-        def __init__(self, model_name, task):
-            self.task = task
-
-            if model_name == 'transformer':
-                self.net = Transformer()
-            elif model_name == 'my_cool_version':
-                self.net = MyCoolVersion()
-
-        def training_step(self, batch, batch_idx):
-            if self.task == 'standard_bert':
-                # do standard bert training with self.net...
-                # return loss
-
-            if self.task == 'my_cool_task':
-                # do my own version with self.net
-                # return loss
-
-
-Case 2: COOLER NOT BERT
------------------------
-
-But if you wanted to try something **completely** different, you'd define a new module for that.
-
-
-.. code-block:: python
-
-    class CoolerNotBERT(pl.LightningModule):
-        def __init__(self):
-            self.net = ...
-
-        def training_step(self, batch, batch_idx):
-            # do some other cool task
-            # return loss
-
-
-Rapid research flow
--------------------
-
-Then you could do rapid research by switching between these two and using the same trainer.
-
-
-.. code-block:: python
-
-    if use_bert:
-        model = BERT()
-    else:
-        model = CoolerNotBERT()
-
-    trainer = Trainer(gpus=4, precision=16)
-    trainer.fit(model)
-
-
-**Notice a few things about this flow:**
-
-1. You're writing pure PyTorch... no unnecessary abstractions or new libraries to learn.
-2. You get free GPU and 16-bit support without writing any of that code in your model.
-3. You also get early stopping, multi-gpu training, 16-bit and MUCH more without coding anything!
-