By John Walkenbach
Excel at Excel with assistance from this bestselling spreadsheet guide
John Walkenbach's identify is synonymous with excellence in laptop books that decipher the complexities of Microsoft Excel. often called "Mr. Spreadsheet," Walkenbach exhibits you the way to maximise the facility of Excel 2013 whereas bringing you on top of things at the most modern positive aspects. This perennial bestseller is totally up to date to hide all of the new beneficial properties of Excel 2013, together with tips on how to navigate the consumer interface, reap the benefits of a variety of dossier codecs, grasp formulation, examine information with PivotTables, and more.
Whether you're an Excel newbie who's seeking to get extra savvy or a sophisticated person trying to develop into an influence person, this newest version provide you with finished assurance in addition to precious assistance, methods, and methods that you just won't locate at any place else. stocks the helpful perception of Excel guru and bestselling writer "Mr. Spreadsheet" John Walkenbach as he courses you thru each element of Excel 2013 offers crucial insurance of all of the latest gains of Excel 2013 provides fabric in a transparent, concise, logical structure that's excellent for all degrees of Excel adventure encompasses a site that comes with downloadable templates and worksheets from the book
Chart your route to fabulous formulation and stellar spreadsheets with "Excel 2013 Bible"!
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Extra resources for Excel 2013 Bible
The whole attribute set is denoted as A = [S , T ]. Thus, the problem is formally defined as: Relational User Attribute Inference. Given a collection of Google+ users U and attribute set A = [S , T ], the goal of relational user attribute inference is to learn (1) a predictive function f (Xu , S ) → Tu to infer the target attribute label of a user; (2) attribute relation compatibility Ψ (ai , ak ) ∈ R|A |×|A | , where Ψ indicate the compatibility strength of attribute relations. In this study, we collect our experimental dataset from Google+ via its publicly accessible API.
The first type of attribute is demographic attribute. 2 Related Work 37 the rich online social multimedia activities, such as search query log, tweets, favorite video list, check-in history, to infer users’ age, gender, occupation, etc. We review the typical work in a chronological order. In , the authors built a demographic model to predict the users’ ages and genders based on their browsing activities. Reference  investigated the problem of inferring user demographics based on their queries from the privacy perspective.
As aforementioned, we study six types of user attributes including gender, age, relationship, occupation, interest, and sentiment orientation. We invited eight active social network users as annotators. Three annotators are assigned for each user record. The annotators are asked to refer to substantial information from Facebook, Wikipedia, Google Search, to accomplish the attribute annotation of each celebrity user. A label is determined as ground-truth if at least two annotators agree on it. 4 shows the distribution of each attribute.