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(The winner of a go game is the player who controls more points at the end of the game, after making a small adjustment to eliminate the first player's advantage.) That gives the networks more information to learn from.
Features of a go position are approximately translation-invariant. In other words, a configuration of stones is about as valuable in one place as another, all else being equal, although it may depend on how close it is to the edge of the board. A network which does not take this into account will have to learn the value of each configuration at each location it can appear.
Therefore the networks learned feature detectors which were scanned over their inputs...
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