README.md in torch-rb-0.2.4 vs README.md in torch-rb-0.2.5

@@ -1,9 +1,11 @@
 # Torch.rb
 
 :fire: Deep learning for Ruby, powered by [LibTorch](https://pytorch.org)
 
+For computer vision tasks, also check out [TorchVision](https://github.com/ankane/torchvision)
+
 [![Build Status](https://travis-ci.org/ankane/torch.rb.svg?branch=master)](https://travis-ci.org/ankane/torch.rb)
 
 ## Installation
 
 First, [install LibTorch](#libtorch-installation). For Homebrew, use:
@@ -20,10 +22,22 @@
 
 It can take a few minutes to compile the extension.
 
 ## Getting Started
 
+Deep learning is significantly faster with a GPU. If you don’t have an NVIDIA GPU, we recommend using a cloud service. [Paperspace](https://www.paperspace.com/) has a great free plan.
+
+We’ve put together a [Docker image](https://github.com/ankane/ml-stack) to make it easy to get started. On Paperspace, create a notebook with a custom container. Set the container name to:
+
+```text
+ankane/ml-stack:torch-gpu
+```
+
+And leave the other fields in that section blank. Once the notebook is running, you can run the [MNIST example](https://github.com/ankane/ml-stack/blob/master/torch-gpu/MNIST.ipynb).
+
+## API
+
 This library follows the [PyTorch API](https://pytorch.org/docs/stable/torch.html). There are a few changes to make it more Ruby-like:
 
 - Methods that perform in-place modifications end with `!` instead of `_` (`add!` instead of `add_`)
 - Methods that return booleans use `?` instead of `is_`  (`tensor?` instead of `is_tensor`)
 - Numo is used instead of NumPy (`x.numo` instead of `x.numpy()`)
@@ -190,11 +204,11 @@
 ### Neural Networks
 
 Define a neural network
 
 ```ruby
-class Net < Torch::NN::Module
+class MyNet < Torch::NN::Module
   def initialize
     super
     @conv1 = Torch::NN::Conv2d.new(1, 6, 3)
     @conv2 = Torch::NN::Conv2d.new(6, 16, 3)
     @fc1 = Torch::NN::Linear.new(16 * 6 * 6, 120)
@@ -224,11 +238,11 @@
 ```
 
 Create an instance of it
 
 ```ruby
-net = Net.new
+net = MyNet.new
 input = Torch.randn(1, 1, 32, 32)
 net.call(input)
 ```
 
 Get trainable parameters
@@ -292,11 +306,11 @@
 ```
 
 Load a model
 
 ```ruby
-net = Net.new
+net = MyNet.new
 net.load_state_dict(Torch.load("net.pth"))
 net.eval
 ```
 
 ### Tensor Creation
@@ -411,12 +425,10 @@
 
 ## Performance
 
 ### Linux
 
-Deep learning is significantly faster on GPUs.
-
-Install [CUDA](https://developer.nvidia.com/cuda-downloads) and [cuDNN](https://developer.nvidia.com/cudnn) and reinstall the gem.
+Deep learning is significantly faster on a GPU. Install [CUDA](https://developer.nvidia.com/cuda-downloads) and [cuDNN](https://developer.nvidia.com/cudnn) and reinstall the gem.
 
 Check if CUDA is available
 
 ```ruby
 Torch::CUDA.available?