F♯ for machine learning essentials :: get up and running with machine learning with F♯ in a fun and functional way /
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, England ; Mumbai [India] :
Packt Publishing,
2016.
|
| Schriftenreihe: | Community experience distilled.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Includes index. |
| Beschreibung: | 1 online resource (194 pages) : color illustrations, tables. |
| ISBN: | 9781783989355 1783989351 |
Internformat
MARC
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| 100 | 1 | |a Mukherjee, Sudipta. | |
| 245 | 1 | 0 | |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. |
| 260 | |a Birmingham, England ; |a Mumbai [India] : |b Packt Publishing, |c 2016. | ||
| 300 | |a 1 online resource (194 pages) : |b color illustrations, tables. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Community Experience Distilled | |
| 500 | |a Includes index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (ebrary, viewed July 29, 2016). | |
| 505 | 0 | |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?. | |
| 505 | 8 | |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. | |
| 505 | 8 | |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. | |
| 650 | 0 | |a F♯ (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh2008001530 | |
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| 830 | 0 | |a Community experience distilled. |0 http://id.loc.gov/authorities/names/no2011030603 | |
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Datensatz im Suchindex
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| callnumber-sort | QA 276.73 F16 M854 42016EB |
| callnumber-subject | QA - Mathematics |
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| contents | 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?. 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. 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. |
| ctrlnum | (OCoLC)961851724 |
| dewey-full | 005.133 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | ZDB-4-EBA-ocn961851724 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:27:29Z |
| institution | BVB |
| isbn | 9781783989355 1783989351 |
| language | English |
| oclc_num | 961851724 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (194 pages) : color illustrations, tables. |
| psigel | ZDB-4-EBA |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Packt Publishing, |
| record_format | marc |
| series | Community experience distilled. |
| series2 | Community Experience Distilled |
| spelling | Mukherjee, Sudipta. 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. Birmingham, England ; Mumbai [India] : Packt Publishing, 2016. 1 online resource (194 pages) : color illustrations, tables. text txt rdacontent computer c rdamedia online resource cr rdacarrier Community Experience Distilled Includes index. Online resource; title from PDF title page (ebrary, viewed July 29, 2016). 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?. 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. 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. F♯ (Computer program language) http://id.loc.gov/authorities/subjects/sh2008001530 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Apprentissage automatique. COMPUTERS / Databases / General. bisacsh F♯ (Computer program language) fast Machine learning fast Herbrich, Ralf. has work: F♯ for machine learning essentials (Text) https://id.oclc.org/worldcat/entity/E39PCGvBMRPryt6HXrxjMQ9TDy https://id.oclc.org/worldcat/ontology/hasWork Print version: Mukherjee, Sudipta. F♯ for machine learning essentials : get up and running with machine learning with F♯ in a fun and functional way. Birmingham, England ; Mumbai, [India] : Packt Publishing, ©2016 x, 169 pages Community experience distilled. 9781783989348 Community experience distilled. http://id.loc.gov/authorities/names/no2011030603 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1191129 Volltext |
| spellingShingle | Mukherjee, Sudipta F♯ for machine learning essentials : get up and running with machine learning with F♯ in a fun and functional way / Community experience distilled. 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?. 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. 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. F♯ (Computer program language) http://id.loc.gov/authorities/subjects/sh2008001530 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Apprentissage automatique. COMPUTERS / Databases / General. bisacsh F♯ (Computer program language) fast Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2008001530 http://id.loc.gov/authorities/subjects/sh85079324 |
| title | F♯ for machine learning essentials : get up and running with machine learning with F♯ in a fun and functional way / |
| title_auth | F♯ for machine learning essentials : get up and running with machine learning with F♯ in a fun and functional way / |
| title_exact_search | F♯ for machine learning essentials : get up and running with machine learning with F♯ in a fun and functional way / |
| title_full | 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. |
| title_fullStr | 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. |
| title_full_unstemmed | 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. |
| title_short | F♯ for machine learning essentials : |
| title_sort | f♯ for machine learning essentials get up and running with machine learning with f♯ in a fun and functional way |
| title_sub | get up and running with machine learning with F♯ in a fun and functional way / |
| topic | F♯ (Computer program language) http://id.loc.gov/authorities/subjects/sh2008001530 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Apprentissage automatique. COMPUTERS / Databases / General. bisacsh F♯ (Computer program language) fast Machine learning fast |
| topic_facet | F♯ (Computer program language) Machine learning. Apprentissage automatique. COMPUTERS / Databases / General. Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1191129 |
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