Merge pull request #73 from jabalazs/fix-links-display

Fix links that were being displayed incorrectly

README.md

@@ -53,7 +53,7 @@
 11.  [Neural Networks - usherbrooke](http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html)
 12.  [Machine Learning - Oxford](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) (2014-2015)
 13.  [Deep Learning - Nvidia](https://developer.nvidia.com/deep-learning-courses) (2015)
-14.  [Graduate Summer School: Deep Learning, Feature Learning] (https://www.youtube.com/playlist?list=PLHyI3Fbmv0SdzMHAy0aN59oYnLy5vyyTA) by Geoffrey Hinton, Yoshua Bengio, Yann LeCun, Andrew Ng, Nando de Freitas and several others @ IPAM, UCLA (2012)
+14.  [Graduate Summer School: Deep Learning, Feature Learning](https://www.youtube.com/playlist?list=PLHyI3Fbmv0SdzMHAy0aN59oYnLy5vyyTA) by Geoffrey Hinton, Yoshua Bengio, Yann LeCun, Andrew Ng, Nando de Freitas and several others @ IPAM, UCLA (2012)
 15.  [Deep Learning - Udacity/Google](https://www.udacity.com/course/deep-learning--ud730) by Vincent Vanhoucke and Arpan Chakraborty (2016)
 16.  [Deep Learning - UWaterloo](https://www.youtube.com/playlist?list=PLehuLRPyt1Hyi78UOkMPWCGRxGcA9NVOE) by Prof. Ali Ghodsi at University of Waterloo (2015)
 17.  [Statistical Machine Learning - CMU](https://www.youtube.com/watch?v=azaLcvuql_g&list=PLjbUi5mgii6BWEUZf7He6nowWvGne_Y8r) by Prof. Larry Wasserman
@@ -97,7 +97,7 @@
 4.  [CMU’s list of papers](http://deeplearning.cs.cmu.edu/)
 5.  [Neural Networks for Named Entity Recognition](http://nlp.stanford.edu/~socherr/pa4_ner.pdf) [zip](http://nlp.stanford.edu/~socherr/pa4-ner.zip)
 6. [Training tricks by YB](http://www.iro.umontreal.ca/~bengioy/papers/YB-tricks.pdf)
-7. [Geoff Hinton's reading list (all papers)] (http://www.cs.toronto.edu/~hinton/deeprefs.html)
+7. [Geoff Hinton's reading list (all papers)](http://www.cs.toronto.edu/~hinton/deeprefs.html)
 8. [Supervised Sequence Labelling with Recurrent Neural Networks](http://www.cs.toronto.edu/~graves/preprint.pdf)
 9.  [Statistical Language Models based on Neural Networks](http://www.fit.vutbr.cz/~imikolov/rnnlm/thesis.pdf)
 10.  [Training Recurrent Neural Networks](http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf)
@@ -125,7 +125,8 @@
 32. [Ask Me Anything: Dynamic Memory Networks for Natural Language Processing](http://arxiv.org/pdf/1506.07285v1.pdf)
 33. [Mastering the Game of Go with Deep Neural Networks and Tree Search](http://www.nature.com/nature/journal/v529/n7587/pdf/nature16961.pdf)
 34. [Batch Normalization](https://arxiv.org/abs/1502.03167)
-36. [Residual Learning](https://arxiv.org/pdf/1512.03385v1.pdf)
+35. [Residual Learning](https://arxiv.org/pdf/1512.03385v1.pdf)
+36. [Image-to-Image Translation with Conditional Adversarial Networks] (https://arxiv.org/pdf/1611.07004v1.pdf) 
 37. [Berkeley AI Research (BAIR) Laboratory] Image-to-Image Translation with Conditional Adversarial Networks (https://arxiv.org/pdf/1611.07004v1.pdf) 
 38. [MobileNets by Google] (https://arxiv.org/abs/1704.04861)
 39. [Cross Audio-Visual Recognition in the Wild Using Deep Learning] (https://arxiv.org/abs/1706.05739)
@@ -143,7 +144,7 @@
 7.  [Neural Networks for Matlab](http://uk.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf)
 8.  [Using convolutional neural nets to detect facial keypoints tutorial](http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/)
 9.  [Torch7 Tutorials](https://github.com/clementfarabet/ipam-tutorials/tree/master/th_tutorials)
-10.  [The Best Machine Learning Tutorials On The Web] (https://github.com/josephmisiti/machine-learning-module)
+10.  [The Best Machine Learning Tutorials On The Web](https://github.com/josephmisiti/machine-learning-module)
 11. [VGG Convolutional Neural Networks Practical](http://www.robots.ox.ac.uk/~vgg/practicals/cnn/index.html)
 12. [TensorFlow tutorials](https://github.com/nlintz/TensorFlow-Tutorials)
 13. [More TensorFlow tutorials](https://github.com/pkmital/tensorflow_tutorials)
@@ -450,9 +451,9 @@
 40.  [Paddle - PArallel Distributed Deep LEarning by Baidu](https://github.com/baidu/paddle)
 41.  [NeuPy - Theano based Python library for ANN and Deep Learning](http://neupy.com)
 42.  [Lasagne - a lightweight library to build and train neural networks in Theano](https://github.com/Lasagne/Lasagne)
-43.  [nolearn - wrappers and abstractions around existing neural network libraries, most notably Lasagne (https://github.com/dnouri/nolearn)
-44.  [Sonnet - a library for constructing neural networks by Google's DeepMind] https://github.com/deepmind/sonnet
-45.  [PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration] https://github.com/pytorch/pytorch
+43.  [nolearn - wrappers and abstractions around existing neural network libraries, most notably Lasagne](https://github.com/dnouri/nolearn)
+44.  [Sonnet - a library for constructing neural networks by Google's DeepMind](https://github.com/deepmind/sonnet)
+45.  [PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration](https://github.com/pytorch/pytorch)
 
 
 ### Miscellaneous
@@ -465,13 +466,13 @@
 6.  [TorontoDeepLEarning convnet](https://github.com/TorontoDeepLearning/convnet)
 8.  [gfx.js](https://github.com/clementfarabet/gfx.js)
 9. [Torch7 Cheat sheet](https://github.com/torch/torch7/wiki/Cheatsheet)
-10. [Misc from MIT's 'Advanced Natural Language Processing' course] (http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-864-advanced-natural-language-processing-fall-2005/)
+10. [Misc from MIT's 'Advanced Natural Language Processing' course](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-864-advanced-natural-language-processing-fall-2005/)
 11. [Misc from MIT's 'Machine Learning' course](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/)
 12. [Misc from MIT's 'Networks for Learning: Regression and Classification' course](http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-520-a-networks-for-learning-regression-and-classification-spring-2001/)
 13. [Misc from MIT's 'Neural Coding and Perception of Sound' course](http://ocw.mit.edu/courses/health-sciences-and-technology/hst-723j-neural-coding-and-perception-of-sound-spring-2005/index.htm)
 14. [Implementing a Distributed Deep Learning Network over Spark](http://www.datasciencecentral.com/profiles/blogs/implementing-a-distributed-deep-learning-network-over-spark)
 15. [A chess AI that learns to play chess using deep learning.](https://github.com/erikbern/deep-pink)
-16. [Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind] (https://github.com/kristjankorjus/Replicating-DeepMind)
+16. [Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind](https://github.com/kristjankorjus/Replicating-DeepMind)
 17. [Wiki2Vec. Getting Word2vec vectors for entities and word from Wikipedia Dumps](https://github.com/idio/wiki2vec)
 18. [The original code from the DeepMind article + tweaks](https://github.com/kuz/DeepMind-Atari-Deep-Q-Learner)
 19. [Google deepdream - Neural Network art](https://github.com/google/deepdream)