updated docs

docs/LightningModule/RequiredTrainerInterface.md

@@ -300,7 +300,7 @@ def tng_dataloader(self)
 Called by lightning during training loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.
 
 ##### Return
-Pytorch DataLoader
+PyTorch DataLoader
 
 **Example**
 
@@ -327,7 +327,7 @@ def tng_dataloader(self)
 Called by lightning during validation loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.
 
 ##### Return
-Pytorch DataLoader
+PyTorch DataLoader
 
 **Example**
 
@@ -355,7 +355,7 @@ def test_dataloader(self)
 Called by lightning during test loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.
 
 ##### Return
-Pytorch DataLoader
+PyTorch DataLoader
 
 **Example**
 

docs/LightningModule/methods.md

@@ -31,7 +31,7 @@ y_hat = pretrained_model(x)
 
 | Param  | description  |
 |---|---|
-|  weights_path | Path to a pytorch checkpoint  |
+|  weights_path | Path to a PyTorch checkpoint  |
 |  tags_csv | Path to meta_tags.csv file generated by the test-tube Experiment  |
 |  on_gpu | if True, puts model on GPU. Make sure to use transforms option if model devices have changed  |
 |  map_location | A dictionary mapping saved weight GPU devices to new GPU devices |

docs/Trainer/Logging.md

@@ -52,7 +52,7 @@ Trainer(experiment=exp)
 
 ---
 ### Tensorboard support   
-The experiment object is a strict subclass of Pytorch SummaryWriter. However, this class
+The experiment object is a strict subclass of PyTorch SummaryWriter. However, this class
 also snapshots every detail about the experiment (data folder paths, code, hyperparams),
 and allows you to visualize it using tensorboard.
 ``` {.python}

docs/Trainer/hooks.md

@@ -5,7 +5,7 @@ There are cases when you might want to do something different at different parts
 To enable a hook, simply override the method in your LightningModule and the trainer will call it at the correct time.
 
 **Contributing** If there's a hook you'd like to add, simply:    
-1. Fork PytorchLightning.    
+1. Fork PyTorchLightning.    
 2. Add the hook [here](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/root_module/hooks.py).       
 3. Add the correct place in the [Trainer](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/models/trainer.py) where it should be called.    
 

docs/Trainer/index.md

@@ -64,7 +64,7 @@ But of course the fun is in all the advanced things it can do:
 - [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
 - [Hooks](hooks)
 - [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
-- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers)
+- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/PyTorch-Lightning/LightningModule/#configure_optimizers)
 - [Set how much of the training set to check (1-100%)](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check)
 
 **Validation loop**    

docs/index.md

@@ -71,7 +71,7 @@ one could be a seq-2-seq model, both (optionally) ran by the same trainer file.
 - [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
 - [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
 - [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
-- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers)
+- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/PyTorch-Lightning/LightningModule/#configure_optimizers)
 - [Set how much of the training set to check (1-100%)](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check)
 
 ###### Validation loop