LEADER 00916nam0-22003011i-450- 001 990002009120403321 005 20040528114518.0 035 $a000200912 035 $aFED01000200912 035 $a(Aleph)000200912FED01 035 $a000200912 100 $a20030910d1913----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ay---a---001yy 200 1 $aParassitologia animale$eanimali parassiti ed animali trasmettitori di malattie parassitarie all'uomo e agli animali domestici$fDavide Carazzi 210 $aMilano$cSocieta Editrice Libraria$d1913 215 $a426 p.$d24 cm 610 0 $aParassitologia$aAnimale 676 $a616.9 700 1$aCarazzi,$bDavide$f<1858-1923>$0360001 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990002009120403321 952 $a61 III D.3/12$b45$fDAGEN 959 $aDAGEN 996 $aParassitologia animale$9402558 997 $aUNINA LEADER 01160nam a2200277 i 4500 001 991002047339707536 008 130507s2010 it b 000 0 ita d 020 $a9788857502571 035 $ab14112528-39ule_inst 040 $aDip.to Studi Umanistici - Sez. Filosofia e Scienze Sociali$bita 041 1 $aita$hger 100 1 $aSimmel, Georg$0121049 245 10$aDenaro e vita :$bsenso e forme dell'esistere /$cGeorg Simmel ; a cura di Francesco Mora 260 $aMilano ;$aUdine :$bMimesis,$cc2010 300 $a115 p. :$bill., ritr. ;$c19 cm 500 $aRaccolta di saggi gią pubblicati 504 $aContiene riferimenti bibliografici 700 1 $aMora, Francesco 830 0$aVolti [Mimesis] ;$v46 907 $a.b14112528$b02-04-14$c07-05-13 912 $a991002047339707536 945 $aLE005 193 SIM 01.02$g1$i2005000353895$lle005$op$pE12.00$q-$rl$s- $t0$u3$v6$w3$x0$y.i15529617$z03-09-13 945 $aLE005 193 SIM 01.02 In Dep. Prof. Tundo$g2$i2005000353901$lle005$op$pE12.00$q-$rn$s- $t0$u0$v0$w0$x0$y.i15529629$z03-09-13 996 $aDenaro e vita$9264402 997 $aUNISALENTO 998 $ale005$b07-05-13$cm$da $e-$fita$git $h0$i0 LEADER 04584nam 2200565 450 001 9910824490003321 005 20170919022152.0 010 $a1-78398-935-1 035 $a(CKB)3710000000604294 035 $a(EBL)4520803 035 $a(MiAaPQ)EBC4520803 035 $z(PPN)220206333 035 $a(PPN)19329379X 035 $a(EXLCZ)993710000000604294 100 $a20160810h20162016 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aF# for machine learning essentials $eget up and running with machine learning with F# in a fun and functional way /$fSudipta Mukherjee ; foreword by Dr. Ralf Herbrich, director of machine learning science at Amazon 205 $a1. 210 1$aBirmingham, England ;$aMumbai, [India] :$cPackt Publishing,$d2016. 210 4$d©2016 215 $a1 online resource (194 p.) 225 1 $aCommunity Experience Distilled 300 $aIncludes index. 311 $a1-78398-934-3 327 $aCover ; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introduction to Machine Learning; Objective; Getting in touch; Different areas where machine learning is being used; Why use F#?; Supervised machine learning; Training and test dataset/corpus; Some motivating real life examples of supervised learning; Nearest Neighbour algorithm (a.k.a k-NN algorithm); Distance metrics; Decision tree algorithms; Unsupervised learning; Machine learning frameworks; Machine learning for fun and profit 327 $aRecognizing handwritten digits - your ""Hello World"" ML programHow does this work?; Summary; Chapter 2: Linear Regression; Objective; Different types of linear regression algorithms; APIs used; Math.NET Numerics for F# 3.7.0; Getting Math.NET; Experimenting with Math.NET; The basics of matrices and vectors (a short and sweet refresher); Creating a vector; Creating a matrix; Finding the transpose of a matrix; Finding the inverse of a matrix; Trace of a matrix; QR decomposition of a matrix; SVD of a matrix; Linear regression method of least square 327 $aFinding linear regression coefficients using F#Finding the linear regression coefficients using Math.NET; Putting it together with Math.NET and FsPlot; Multiple linear regression; Multiple linear regression and variations using Math.NET; Weighted linear regression; Plotting the result of multiple linear regression; Ridge regression; Multivariate multiple linear regression; Feature scaling; Summary; Chapter 3: Classification Techniques; Objective; Different classification algorithms you will learn; Some interesting things you can do; Binary classification using k-NN; How does it work? 327 $aFinding cancerous cells using k-NN: a case studyUnderstanding logistic regression ; The sigmoid function chart; Binary classification using logistic regression (using Accord.NET); Multiclass classification using logistic regression; How does it work?; Multiclass classification using decision trees; Obtaining and using WekaSharp; How does it work?; Predicting a traffic jam using a decision tree: a case study; Challenge yourself!; Summary; Chapter 4: Information Retrieval; Objective; Different IR algorithms you will learn; What interesting things can you do? 327 $aInformation retrieval using tf-idfMeasures of similarity; Generating a PDF from a histogram; Minkowski family; L1 family; Intersection family; Inner Product family; Fidelity family or squared-chord family; Squared L2 family; Shannon's Entropy family; Similarity of asymmetric binary attributes; Some example usages of distance metrics; Finding similar cookies using asymmetric binary similarity measures; Grouping/clustering color images based on Canberra distance; Summary; Chapter 5: Collaborative Filtering; Objective; Different classification algorithms you will learn 327 $aVocabulary of collaborative filtering 410 0$aCommunity experience distilled. 517 3 $aF sharp for machine learning essentials 606 $aF# (Computer program language) 606 $aMachine learning 615 0$aF# (Computer program language) 615 0$aMachine learning. 676 $a005.133 700 $aMukherjee$b Sudipta$0892441 702 $aHerbrich$b Ralf 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910824490003321 996 $aF# for machine learning essentials$94013458 997 $aUNINA