Adds some syntax highlighting

README.md

@@ -34,9 +34,10 @@ And, therefore relieves the network designer of having to commit to a potentiall
 We support ***MxNet, Keras, and PyTorch***. Instructions for MxNet can be found [here](rational/mxnet). Instructions for Keras [here](rational/keras).
 The following README instructions **assume that you want to use rational activations in PyTorch.**
 
-    PyTorch>=1.4.0
-    CUDA>=10.2
-
+```
+PyTorch>=1.4.0
+CUDA>=10.2
+```
 
 ## 3. Installation
 
@@ -47,28 +48,31 @@ To install the rational_activations module, you can use pip, but:<br/>
 For non TensorFlow and MXNet users, or **if the command bellow don't work** the package listed bellow don't work on your machine:
 #### TensorFlow or MXNet (and `torch==1.9.0`)
 
-     pip3 install -U pip wheel
-     pip3 install torch rational_activations
+```shell
+pip3 install -U pip wheel
+pip3 install torch rational_activations
+```
 
 #### Other CUDA/Pytorch</h3>
 
 For any other torch version, please install from source:
 Modify requirements.txt to your corresponding torch version
 
-     pip3 install airspeed  # to compile the CUDA templates
-     git clone https://github.com/ml-research/rational_activations.git
-     cd rational_activations
-     pip3 install -r requirements.txt --user
-     python3 setup.py install --user
-
+```shell
+pip3 install airspeed  # to compile the CUDA templates
+git clone https://github.com/ml-research/rational_activations.git
+cd rational_activations
+pip3 install -r requirements.txt --user
+python3 setup.py install --user
+```
 
 If you encounter any trouble installing rational, please contact [this person]([email protected]).
 
 ## 4. Using Rational in Neural Networks
 
 Rational can be integrated in the same way as any other common activation function.
 
-~~~~
+```python
 import torch
 from rational.torch import Rational
 
@@ -77,12 +81,13 @@ model = torch.nn.Sequential(
     Rational(), # e.g. instead of torch.nn.ReLU()
     torch.nn.Linear(H, D_out),
 )
-~~~~
+```
 
 Please also check [the documentation :notebook_with_decorative_cover:	](https://rational-activations.readthedocs.io/en/latest/)
 
 ## 5. Cite Us in your paper
-```
+
+```bibtex
 @inproceedings{molina2019pade,
   title={Pad{\'e} Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks},
   author={Molina, Alejandro and Schramowski, Patrick and Kersting, Kristian},