Update README.md

README.md

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 Extrinsically, the neural network provides a prediction interval with upper and lower bounds that provides an estimate of the amount of uncertainty in prediction. This research shows that removing the SEM node with the weakest causal connection to firm performance speeds up the time to finding that estimate when uncertainty is added to the neural network using the Kullback-Leibler divergence. This opens up the possibility of estimating SEM in this manner after each epoch in a manner similar to [Dropout](https://patents.google.com/patent/US9406017B2/en) in neural networks, which might be considered 'prior art' to this technique. However, this would apply to nodes/edges instead:
 
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   <img width="400" height="300" src=https://mir-s3-cdn-cf.behance.net/projects/404/11278773.54812a20214b7.jpg>
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-*Image credit Behance.net, creative commons license*
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 This code uses version 0.7.0 of Tensorflow-probability and Scikit-Learn to organize the experiments.