Update README.md

README.md

@@ -60,11 +60,7 @@ The Omniglot data set is designed for developing more human-like learning algori
 
 ## Open Binary Omniglot notebook:
 
-<<<<<<< HEAD
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CF5S1hTIczb4k2hzOyZr5x6ItUeTYrj3#scrollTo=VY8t9rlv-P7q&uniqifier=1)
-=======
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CF5S1hTIczb4k2hzOyZr5x6ItUeTYrj3)
->>>>>>> a9f26e83c3213b3749c07a7d7bbe0c9d84d86ca3
 
 ## Results on MNIST:
 ### Loss/ACC: with 10 epochs.
@@ -105,10 +101,6 @@ In this part, we present results obtained with [Matching Networks for One Shot L
 | binary MN: fourth conv           	|     53.6     	|
 
 
-<<<<<<< HEAD
-
-=======
->>>>>>> a9f26e83c3213b3749c07a7d7bbe0c9d84d86ca3
 # References: 
 * [1]: [Github: Wizaron/binary-stochastic-neurons](https://github.com/Wizaron/binary-stochastic-neurons).
 * [2]: ['HIERARCHICAL MULTISCALE RECURRENT NEURAL NETWORKS', Junyoung Chung, Sungjin Ahn & Yoshua Bengio (Mar 2017)](https://arxiv.org/pdf/1609.01704.pdf).