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[null,"1.","Record Nr.",null,"UNINA9910824490003321"]
[null,null,"Autore",null,"Mukherjee Sudipta"]
[null,null,"Titolo",null,"F# 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 Amazon"]
[null,null,"Pubbl/distr/stampa",null,"Birmingham, England ; ; Mumbai, [India] : , : Packt Publishing, , 2016"]
[{"$nulls":4},"©2016"]
[null,null,"ISBN",null,"1-78398-935-1"]
[null,null,"Edizione",null,"[1.]"]
[null,null,"Descrizione fisica",null,"1 online resource (194 p.)"]
[null,null,"Collana",null,"Community Experience Distilled"]
[null,null,"Disciplina",null,"005.133"]
[null,null,"Soggetti",null,"F# (Computer program language)"]
[{"$nulls":4},"Machine learning"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"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 profit"]
[{"$nulls":4},"Recognizing handwritten digits - your \"\"Hello World\"\" ML programHow does this work?; Summary; Chapter 2: Linear Regression; Objective; Different types of linear regression algorithms; APIs used; Math.NET Numerics for F# 3.7.0; Getting Math.NET; Experimenting with Math.NET; The basics of matrices and vectors  (a short and sweet refresher); Creating a vector; Creating a matrix; Finding the transpose of a matrix; Finding the inverse of a matrix; Trace of a matrix; QR decomposition of a matrix; SVD of a matrix; Linear regression method of least square"]
[{"$nulls":4},"Finding 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: "]
[{"$nulls":4},"Classification Techniques; Objective; Different classification algorithms you will learn; Some interesting things you can do; Binary classification using k-NN; How does it work?"]
[{"$nulls":4},"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?"]
[{"$nulls":4},"Information retrieval using tf-idfMeasures of similarity; Generating a PDF from a histogram; Minkowski family; L1 family; Intersection family; Inner Product family; Fidelity family or squared-chord family; Squared L2 family; Shannon's Entropy family; Similarity of asymmetric binary attributes; Some example usages of distance metrics; Finding similar cookies using asymmetric binary similarity measures; Grouping/clustering color images based on Canberra distance; Summary; Chapter 5: Collaborative Filtering; Objective; Different classification algorithms you will learn"]
[{"$nulls":4},"Vocabulary of collaborative filtering"]