Update README.md

README.md

@@ -100,7 +100,7 @@ libfaceid was created to somehow address these problems and fill-in the gaps fro
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 libfaceid is designed so that it is easy to use, modular and robust. Selection of model is done via the constructors while the expose function is simply detect() or estimate() making usage very easy. The files are organized into modules so it is very intuitive to understand and debug. The robust design allows supporting new models in the future to be very straightforward.
 
-Only pretrained models will be supported. Transfer learning is the practice of applying a pretrained model (that is trained on a very large dataset) to a new dataset. It basically means that it is able to generalize models from one dataset to another when it has been trained on a very large dataset, such that it is 'experienced' enough to generalize the learnings to new environment to new datasets. It is one of the major factors in the explosion of popularity in Computer Vision, not only for face recognition but most specially for object detection. And just recently, mid-2018 this year, transfer learning has been making good advances to Natural Language Processing ( [BERT by Google](https://github.com/google-research/bert) and [ELMo by Allen Institute](https://allennlp.org/elmo) ). Transfer learning is really useful and it is the main goal that the community working on Reinforcement Learning wants to achieve for robotics.
+Only pretrained models will be supported. [Transfer learning](http://cs231n.github.io/transfer-learning/) is the practice of applying a pretrained model (that is trained on a very large dataset) to a new dataset. It basically means that it is able to generalize models from one dataset to another when it has been trained on a very large dataset, such that it is 'experienced' enough to generalize the learnings to new environment to new datasets. It is one of the major factors in the explosion of popularity in Computer Vision, not only for face recognition but most specially for object detection. And just recently, mid-2018 this year, transfer learning has been making good advances to Natural Language Processing ( [BERT by Google](https://github.com/google-research/bert) and [ELMo by Allen Institute](https://allennlp.org/elmo) ). Transfer learning is really useful and it is the main goal that the community working on Reinforcement Learning wants to achieve for robotics.
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