added links to readme.

README.md

@@ -6,11 +6,11 @@
 
 
 
-MPoL is a PyTorch *library* built for Regularized Maximum Likelihood (RML) imaging and Bayesian Inference with datasets from interferometers like the Atacama Large Millimeter/Submillimeter Array (ALMA) and the Karl G. Jansky Very Large Array (VLA). 
+MPoL is a [PyTorch](https://pytorch.org/) *library* built for Regularized Maximum Likelihood (RML) imaging and Bayesian Inference with datasets from interferometers like the Atacama Large Millimeter/Submillimeter Array ([ALMA](https://www.almaobservatory.org/en/home/)) and the Karl G. Jansky Very Large Array ([VLA](https://public.nrao.edu/telescopes/vla/)). 
 
-As a PyTorch *library*, MPoL is designed expecting that the user will write Python code that uses MPoL primitives as building blocks to solve their interferometric imaging workflow, much the same way the artificial intelligence community writes Python code that uses PyTorch layers to implement new neural network architectures. You find MPoL easiest to use if you adhere to PyTorch customs and idioms, e.g., for feed-forward neural networks, data storage, GPU acceleration, and train/test optimization loops. 
+As a PyTorch *library*, MPoL is designed expecting that the user will write Python code that uses MPoL primitives as building blocks to solve their interferometric imaging workflow, much the same way the artificial intelligence community writes Python code that uses PyTorch layers to implement new neural network architectures (for [example](https://github.com/pytorch/examples)). You will find MPoL easiest to use if you adhere to PyTorch customs and idioms, e.g., feed-forward neural networks, data storage, GPU acceleration, and train/test optimization loops. Therefore, a basic familiarity with PyTorch is considered a prerequisite for MPoL.
 
-MPoL is *not* an imaging application nor a pipeline, though such programs could be built for specialized workflows using MPoL components. We are focused on providing a numerically correct and expressive set of core primitives such that the user can leverage the full power of the PyTorch (and Python) ecosystem to solve their research-grade imaging tasks. This is already a significant development and maintenance burden for our small research team, and so our immediate scope must necessarily be limited to this objective.
+MPoL is *not* an imaging application nor a pipeline, though such programs could be built for specialized workflows using MPoL components. We are focused on providing a numerically correct and expressive set of core primitives so the user can leverage the full power of the PyTorch (and Python) ecosystem to solve their research-grade imaging tasks. This is already a significant development and maintenance burden for our small research team, so our immediate scope must necessarily be limited.
 
 Installation instructions, documentation, and examples at: [https://mpol-dev.github.io/MPoL/](https://mpol-dev.github.io/MPoL/)