![]() ![]() That's all I have! Your deck does look pretty nice for being like $70 so take my comment as suggestions, not necessary replacements. Search this Thread Tools Jump to Forum Jarad, and the Dredgemator Machine - Let the bodies pile at the door (A Combo DredgeReanimator Deck) 1 Nov 17, 2015. ] ] ] and ] are all super cheap cards that generate great value in this deck too. Commander (EDH) Multiplayer Commander Decklists Jarad, and the Dredgemator Machine - Let the bodies pile at the door (A Combo DredgeReanimator Deck). Also ] is one of the best recursion cards imo. Dumping half of your deck into your graveyard to recur it makes for. View User Profile View Posts Send Message. Jarad, the incredible elf-lich lord 1 Dec 17, 2014. ] is the best option but not budget friendly. Golgari »'Under the command of the elvish lich lord Jarad, the Golgari have dwelled in darkness long enough. I'd add more creature ramp, like ] and ]. You still get the removal, but if you end up milling it you'll make use of Jarad's first ability. For example, I'd probably remove the ] and put in something like ] or ] instead. ![]() The only necessary card you don't have is ] which is like $10 but that'll only add cost so I'm not sure if it helps.Īnother tip for Jarad is to replace sorceries and instants with creatures with similar effects when possible. I don't think you'll get the price down much. Will also delete on comment score of -1 or less.My deck isn't exactly budget but maybe my decklist will give you an idea or two: Parent commenter can toggle NSFW or delete. Interesting: Recommender system | Slope One | Preference elicitation | Reputation system The remainder of this discussion focuses on collaborative filtering for user data, although some of the methods and approaches may apply to the other major applications as well. Collaborative filtering methods have been applied to many different kinds of data including: sensing and monitoring data, such as in mineral exploration, environmental sensing over large areas or multiple sensors financial data, such as financial service institutions that integrate many financial sources or in electronic commerce and web applications where the focus is on user data, etc. Applications of collaborative filtering typically involve very large data sets. In general, collaborative filtering is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Collaborative filtering has two senses, a narrow one and a more general one. ![]() ![]() Collaborative filtering ( CF) is a technique used by some recommender systems. ![]()
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