04584nam 2200565 450 991082449000332120170919022152.01-78398-935-1(CKB)3710000000604294(EBL)4520803(MiAaPQ)EBC4520803(PPN)220206333(PPN)19329379X(EXLCZ)99371000000060429420160810h20162016 uy 0engur|n|---|||||rdacontentrdamediardacarrierF# for machine learning essentials get up and running with machine learning with F# in a fun and functional way /Sudipta Mukherjee ; foreword by Dr. Ralf Herbrich, director of machine learning science at Amazon1.Birmingham, England ;Mumbai, [India] :Packt Publishing,2016.©20161 online resource (194 p.)Community Experience DistilledIncludes index.1-78398-934-3 Cover ; 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 profitRecognizing 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 squareFinding 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?Finding 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?Information 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 learnVocabulary of collaborative filteringCommunity experience distilled.F sharp for machine learning essentialsF# (Computer program language)Machine learningF# (Computer program language)Machine learning.005.133Mukherjee Sudipta892441Herbrich RalfMiAaPQMiAaPQMiAaPQBOOK9910824490003321F# for machine learning essentials4013458UNINA