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|b .M854 2016eb
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|2 bisacsh
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|a 005.133
|2 23
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|a UAMI
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| 100 |
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|a Mukherjee, Sudipta.
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| 245 |
1 |
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|a F♯ for machine learning essentials :
|b get up and running with machine learning with F♯ in a fun and functional way /
|c Sudipta Mukherjee ; foreword by Dr. Ralf Herbrich, director of machine learning science at Amazon.
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| 260 |
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|a Birmingham, England ;
|a Mumbai [India] :
|b Packt Publishing,
|c 2016.
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| 300 |
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|a 1 online resource (194 pages) :
|b color illustrations, tables.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Community Experience Distilled
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| 500 |
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|a Includes index.
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| 588 |
0 |
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|a Online resource; title from PDF title page (ebrary, viewed July 29, 2016).
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| 505 |
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|a 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 -- Recognizing handwritten digits -- your "Hello World" ML program -- How 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 -- 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: Classification Techniques -- Objective -- Different classification algorithms you will learn -- Some interesting things you can do -- Binary classification using k-NN -- How does it work?.
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8 |
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|a Finding cancerous cells using k-NN: a case study -- Understanding 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-idf -- Measures 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 -- Vocabulary of collaborative filtering -- Baseline predictors -- Basis of User-User collaborative filtering -- Implementing basic user-user collaborative filtering using F# -- Code walkthrough -- Variations of gap calculations and similarity measures -- Item-item collaborative filtering -- Top-N recommendations -- Evaluating recommendations -- Prediction accuracy -- Confusion matrix (decision support) -- Ranking accuracy metrics -- Prediction-rating correlation -- Working with real movie review data (Movie Lens) -- Summary -- Chapter 6: Sentiment Analysis -- Objective -- What you will learn -- A baseline algorithm for SA using SentiWordNet lexicons.
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| 505 |
8 |
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|a Handling negations -- Identifying praise or criticism with sentiment orientation -- Pointwise Mutual Information -- Using SO-PMI to find sentiment analysis -- Summary -- Chapter 7: Anomaly Detection -- Objective -- Different classification algorithms -- Some cool things you will do -- The different types of anomalies -- Detecting point anomalies using IQR (Interquartile Range) -- Detecting point anomalies using Grubb's test -- Grubb's test for multivariate data using Mahalanobis distance -- Code walkthrough -- Chi-squared statistic to determine anomalies -- Detecting anomalies using density estimation -- Strategy to convert a collective anomaly to a point anomaly problem -- Dealing with categorical data in collective anomalies -- Summary -- Index.
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| 590 |
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|a eBooks on EBSCOhost
|b EBSCO eBook Subscription Academic Collection - Worldwide
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| 650 |
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0 |
|a F♯ (Computer program language)
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| 650 |
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0 |
|a Machine learning.
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| 650 |
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6 |
|a Apprentissage automatique.
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| 650 |
|
7 |
|a COMPUTERS / Databases / General.
|2 bisacsh
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| 650 |
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7 |
|a F♯ (Computer program language)
|2 fast
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| 650 |
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7 |
|a Machine learning
|2 fast
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| 700 |
1 |
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|a Herbrich, Ralf.
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| 776 |
0 |
8 |
|i Print version:
|a Mukherjee, Sudipta.
|t F♯ for machine learning essentials : get up and running with machine learning with F♯ in a fun and functional way.
|d Birmingham, England ; Mumbai, [India] : Packt Publishing, ©2016
|h x, 169 pages
|k Community experience distilled.
|z 9781783989348
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| 830 |
|
0 |
|a Community experience distilled.
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|n 1191129
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| 994 |
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|a 92
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