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  2. Minecraft modding - Wikipedia

    en.wikipedia.org/wiki/Minecraft_modding

    A Minecraft mod is a mod that changes aspects of the sandbox game Minecraft. Minecraft mods can add additional content to the game, make tweaks to specific features, and optimize performance. Thousands of mods for the game have been created, with some mods even generating an income for their authors.

  3. Dwarf Fortress - Wikipedia

    en.wikipedia.org/wiki/Dwarf_Fortress

    Adams took some time off Armok to work on small side-projects, and another one which would inspire Dwarf Fortress was Mutant Miner. It was turn-based , loosely inspired by a game called Miner VGA . Mutant Miner involved the player digging underneath buildings, searching for ores, fighting monsters, and carrying radioactive "goo" back to the ...

  4. List of Source mods - Wikipedia

    en.wikipedia.org/wiki/List_of_Source_mods

    The mod won ModDB 2010 Mod of the Year awards for Best Original Art, [8] Best Singleplayer Mod [9] and Player's Choice Mod of the Year [10] categories, eventually winning 2nd place at the latter. Operation Black Mesa - An upcoming remake of the Half-Life expansion Opposing Force developed by Tripmine Studio.

  5. Steve (Minecraft) - Wikipedia

    en.wikipedia.org/wiki/Steve_(Minecraft)

    Steve is one of nine default character skins that are available to new players of Minecraft. [16] [17] A skin is the appearance of the player's avatar that represents the player in the game world, which can be changed, altered, or replaced by the player. [18]

  6. Gold mining - Wikipedia

    en.wikipedia.org/wiki/Gold_mining

    A miner underground at Pumsaint gold mine, Wales; c. 1938 Landscape of Las Médulas, Spain, the result of hydraulic mining on a vast scale by the Ancient Romans. The exact date that humans first began to mine gold is unknown, but some of the oldest known gold artifacts were found in the Varna Necropolis in Bulgaria.

  7. Neural network (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Neural_network_(machine...

    Neural networks are typically trained through empirical risk minimization.This method is based on the idea of optimizing the network's parameters to minimize the difference, or empirical risk, between the predicted output and the actual target values in a given dataset. [4]