Fix broken link

Author: rawktron

content/post/build_ml_powered_game_ai_tensorflow.md

@@ -8,7 +8,7 @@ draft = false
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 *Based on a lightning talk given at GopherCon 2017 "Building an ML-Powered Game AI using TensorFlow in Go" [Video](https://www.youtube.com/watch?v=oiorteQg9n0&t=53s) / [Slides](https://github.com/gophercon/2017-talks/tree/master/lightningtalks/PeteGarcin-BuildingMLPoweredGameAIwithTensorFlow)*
 
-(Author: Pete Garcin, Developer Advocate @ ActiveState, @rawktron on [Twitter](https://twitter.com/rawktron) and @peteg on Gophers Slack) 
+(Author: Pete Garcin, Developer Advocate @ [ActiveState](https://activestate.com), @rawktron on [Twitter](https://twitter.com/rawktron) and @peteg on Gophers Slack) 
 
 For GopherCon, we wanted to demonstrate some of the capabilities of the emerging machine learning and data science ecosystem in Go. Originally built as a demo for PyCon, I had put together a simple arcade space shooter game that features enemies powered by machine learning. It was a fun way to get folks engaged at conferences and to learn about the growing library of tools that are available. It also gave me an opportunity to build something non-trivial using machine learning techniques, and my background in games made this kind of interactive demo a good fit.
 
@@ -19,7 +19,7 @@ Go is one of the languages that has a TensorFlow client available, and so it was
 
 It is worth noting that the Go TensorFlow client currently does not support training models, and so we relied on a model that was previously trained using the Python version of the game and then exported using the `SavedModelBuilder` functionality in order to load it in Go. This will export a TensorFlow graph as a protocol buffer and allow it to be loaded in Go using the `LoadSavedModel` function.
 
-For the game portion, I used a library called [Pixel](github.com/faiface/pixel) which is still early in development but has a really active community, and offered excellent stability and performance. I was pretty performance conscious when building and porting the game, so there are certain limitations such as non-pixel-perfect collisions, in order to ensure that the game could run acceptably under all conditions.
+For the game portion, I used a library called [Pixel](https://github.com/faiface/pixel) which is still early in development but has a really active community, and offered excellent stability and performance. I was pretty performance conscious when building and porting the game, so there are certain limitations such as non-pixel-perfect collisions, in order to ensure that the game could run acceptably under all conditions.
 
 ### Training the Neural Net
 Our Neural Net is ultimately a very simple one -- four inputs and a single output neuron. It will use supervised learning to do binary classification on a simple problem: was each shot a hit or a miss? It utilizes the delta between player and enemy position, and player and enemy velocity as the inputs. The single output neuron will fire if its activation value is >= 0.5 and will not fire if it is < 0.5.