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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
| 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 |
| Autore | Mukherjee Sudipta |
| Edizione | [1.] |
| Pubbl/distr/stampa | Birmingham, England ; ; Mumbai, [India] : , : Packt Publishing, , 2016 |
| Descrizione fisica | 1 online resource (194 p.) |
| Disciplina | 005.133 |
| Collana | Community Experience Distilled |
| Soggetto topico |
F# (Computer program language)
Machine learning |
| ISBN | 1-78398-935-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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 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 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 learn Vocabulary of collaborative filtering |
| Altri titoli varianti | F sharp for machine learning essentials |
| Record Nr. | UNINA-9910798003903321 |
Mukherjee Sudipta
|
||
| Birmingham, England ; ; Mumbai, [India] : , : Packt Publishing, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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
| 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 |
| Autore | Mukherjee Sudipta |
| Edizione | [1.] |
| Pubbl/distr/stampa | Birmingham, England ; ; Mumbai, [India] : , : Packt Publishing, , 2016 |
| Descrizione fisica | 1 online resource (194 p.) |
| Disciplina | 005.133 |
| Collana | Community Experience Distilled |
| Soggetto topico |
F# (Computer program language)
Machine learning |
| ISBN | 1-78398-935-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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 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 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 learn Vocabulary of collaborative filtering |
| Altri titoli varianti | F sharp for machine learning essentials |
| Record Nr. | UNINA-9910824490003321 |
|
Mukherjee Sudipta
|
||
| Birmingham, England ; ; Mumbai, [India] : , : Packt Publishing, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
ML.NET revealed : simple tools for applying machine learning to your applications / / Sudipta Mukherjee
| ML.NET revealed : simple tools for applying machine learning to your applications / / Sudipta Mukherjee |
| Autore | Mukherjee Sudipta |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | [Place of publication not identified] : , : Apress, , [2021] |
| Descrizione fisica | 1 online resource (XVIII, 174 p. 160 illus.) |
| Disciplina | 006.31 |
| Soggetto topico | Machine learning |
| ISBN | 1-4842-6543-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Meet ML.NET -- Chapter 2: The Pipeline -- Chapter 3: Handling Data -- Chapter 4: Regressions -- Chapter 5: Classifications -- Chapter 6: Clustering -- Chapter 7: Sentiment Analysis -- Chapter 8: Product Recommendation -- Chapter 9: Anomaly Detection -- Chapter 10: Object Detection. |
| Record Nr. | UNINA-9910484971503321 |
|
Mukherjee Sudipta
|
||
| [Place of publication not identified] : , : Apress, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
NET 4.0 Generics [[electronic resource] ] : beginner's guide : enhance the type safety of your code and create applications easily using Generics in the .NET 4.0 Framework / / Sudipta Mukherjee
| NET 4.0 Generics [[electronic resource] ] : beginner's guide : enhance the type safety of your code and create applications easily using Generics in the .NET 4.0 Framework / / Sudipta Mukherjee |
| Autore | Mukherjee Sudipta |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham, U.K., : Packt Pub., 2012 |
| Descrizione fisica | 1 online resource (396 p.) |
| Disciplina | 005.2768 |
| Soggetto topico |
Generic programming (Computer science)
Microsoft .NET Microsoft .NET Framework |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-45353-3
9786613453532 1-84969-079-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Copyright; Credits; Foreword; About the Author; Acknowledgement; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Why Generics?; An analogy; Reason 1: Generics can save you a lot of typing; Reason 2: Generics can save you type safety woes, big time; What's the problem with this approach?; Reason 3: Generics leads to faster code; Reason 4: Generics is now ubiquitous in the .NET ecosystem; Setting up the environment; Summary; Chapter 2: Lists; Why bother learning about generic lists?; Types of generic lists; Checking whether a sequence is a palindrome or not
Time for action - creating the generic stack as the bufferTime for action - completing the rest of the method; Designing a generic anagram finder; Time for action - creating the method; Life is full of priorities, let's bring some order there; Time for action - creating the data structure for the prioritized shopping list; Time for action - let's add some gadgets to the list and see them; Time for action - let's strike off the gadgets with top-most priority after we have bought them; Time for action - let's create an appointment list; Live sorting and statistics for online bidding Time for action - let's create a custom class for live sortingWhy did we have three LinkedList as part of the data structure?; An attempt to answer questions asked by your boss; Time for action - associating products with live sorted bid amounts; Time for action - finding common values across different bidding amount lists; You will win every scrabble game from now on; Time for action - creating the method to find the character histogram of a word; Time for action - checking whether a word can be formed; Time for action - let's see whether it works Trying to fix an appointment with a doctor?Time for action - creating a set of dates of the doctors' availability; Time for action - finding out when both doctors shall be present; Revisiting the anagram problem; Time for action - re-creating the anagram finder; Lists under the hood; Summary; Chapter 3: Dictionaries; Types of generic associative structures; Creating a tag cloud generator using dictionary; Time for action - creating the word histogram; Creating a bubble wrap popper game; Time for action - creating the game console; Look how easy it was! How did we decide we need a dictionary and not a list?Let's build a generic autocomplete service; Time for action - creating a custom dictionary for autocomplete; Time for action - creating a class for autocomplete; The most common pitfall. Don't fall there!; Let's play some piano; Time for action - creating the keys of the piano; How are we recording the key strokes?; Time for action - switching on recording and playing recorded keystrokes; How it works?; C# Dictionaries can help detect cancer. Let's see how!; Time for action - creating the KNN API Time for action - getting the patient records |
| Record Nr. | UNINA-9910457513803321 |
|
Mukherjee Sudipta
|
||
| Birmingham, U.K., : Packt Pub., 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
NET 4.0 Generics [[electronic resource] ] : beginner's guide : enhance the type safety of your code and create applications easily using Generics in the .NET 4.0 Framework / / Sudipta Mukherjee
| NET 4.0 Generics [[electronic resource] ] : beginner's guide : enhance the type safety of your code and create applications easily using Generics in the .NET 4.0 Framework / / Sudipta Mukherjee |
| Autore | Mukherjee Sudipta |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham, U.K., : Packt Pub., 2012 |
| Descrizione fisica | 1 online resource (396 p.) |
| Disciplina | 005.2768 |
| Soggetto topico |
Generic programming (Computer science)
Microsoft .NET Microsoft .NET Framework |
| ISBN |
1-283-45353-3
9786613453532 1-84969-079-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Copyright; Credits; Foreword; About the Author; Acknowledgement; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Why Generics?; An analogy; Reason 1: Generics can save you a lot of typing; Reason 2: Generics can save you type safety woes, big time; What's the problem with this approach?; Reason 3: Generics leads to faster code; Reason 4: Generics is now ubiquitous in the .NET ecosystem; Setting up the environment; Summary; Chapter 2: Lists; Why bother learning about generic lists?; Types of generic lists; Checking whether a sequence is a palindrome or not
Time for action - creating the generic stack as the bufferTime for action - completing the rest of the method; Designing a generic anagram finder; Time for action - creating the method; Life is full of priorities, let's bring some order there; Time for action - creating the data structure for the prioritized shopping list; Time for action - let's add some gadgets to the list and see them; Time for action - let's strike off the gadgets with top-most priority after we have bought them; Time for action - let's create an appointment list; Live sorting and statistics for online bidding Time for action - let's create a custom class for live sortingWhy did we have three LinkedList as part of the data structure?; An attempt to answer questions asked by your boss; Time for action - associating products with live sorted bid amounts; Time for action - finding common values across different bidding amount lists; You will win every scrabble game from now on; Time for action - creating the method to find the character histogram of a word; Time for action - checking whether a word can be formed; Time for action - let's see whether it works Trying to fix an appointment with a doctor?Time for action - creating a set of dates of the doctors' availability; Time for action - finding out when both doctors shall be present; Revisiting the anagram problem; Time for action - re-creating the anagram finder; Lists under the hood; Summary; Chapter 3: Dictionaries; Types of generic associative structures; Creating a tag cloud generator using dictionary; Time for action - creating the word histogram; Creating a bubble wrap popper game; Time for action - creating the game console; Look how easy it was! How did we decide we need a dictionary and not a list?Let's build a generic autocomplete service; Time for action - creating a custom dictionary for autocomplete; Time for action - creating a class for autocomplete; The most common pitfall. Don't fall there!; Let's play some piano; Time for action - creating the keys of the piano; How are we recording the key strokes?; Time for action - switching on recording and playing recorded keystrokes; How it works?; C# Dictionaries can help detect cancer. Let's see how!; Time for action - creating the KNN API Time for action - getting the patient records |
| Record Nr. | UNINA-9910779071103321 |
|
Mukherjee Sudipta
|
||
| Birmingham, U.K., : Packt Pub., 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Source Code Analytics With Roslyn and JavaScript Data Visualization / / by Sudipta Mukherjee
| Source Code Analytics With Roslyn and JavaScript Data Visualization / / by Sudipta Mukherjee |
| Autore | Mukherjee Sudipta |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2016 |
| Descrizione fisica | 1 online resource (XXI, 170 p. 129 illus., 122 illus. in color.) |
| Disciplina | 005.11 |
| Soggetto topico |
Computer programming
Software engineering Programming languages (Electronic computers) Programming Techniques Software Engineering Programming Languages, Compilers, Interpreters |
| ISBN |
9781484219256
1484219252 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1 Meet Roslyn Syntax -- Chapter 2 Code Quality Metrics -- Chapter 3 Design Quality Metrics -- Chapter 4 Code Performance Metrics -- Chapter 5 Code Mining -- Chapter 6 Code Forensics -- Chapter 7 Code Visualization. |
| Record Nr. | UNINA-9910254748903321 |
|
Mukherjee Sudipta
|
||
| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Thinking in LINQ : Harnessing the Power of Functional Programming in .NET Applications / / by Sudipta Mukherjee
| Thinking in LINQ : Harnessing the Power of Functional Programming in .NET Applications / / by Sudipta Mukherjee |
| Autore | Mukherjee Sudipta |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2014 |
| Descrizione fisica | 1 online resource (259 p.) |
| Disciplina | 006.7882 |
| Collana | Expert's Voice In Networking |
| Soggetto topico |
Microsoft software
Microsoft .NET Framework Software engineering Microsoft and .NET Software Engineering/Programming and Operating Systems |
| ISBN |
9781430268444
1430268441 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents at a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Thinking Functionally; 1-1. Understanding Functional Programming; 1-2. Using Func in C# to Represent Functions; 1-3. Using Various Types of Functions; Generator Functions; Statistical Functions; Projector Functions; Filters; 1-4. Understanding the Benefits of Functional Programming; Composability; Lazy Evaluation; Immutability; Parallelizable; Declarative; 1-5. Getting LINQPad; Chapter 2: Series Generation; 2-1. Math and Statistics: Finding the Dot Product of Two Vectors
ProblemSolution; How It Works; 2-2. Math and Statistics: Generating Pythagorean Triples; Problem; Solution; How It Works; 2-3. Math and Statistics: Finding a Weighted Sum; Problem; Solution; How It Works; 2-4. Math and Statistics: Finding the Percentile for Each Element in an Array of Numbers; Problem; Solution; How It Works; 2-5. Math and Statistics: Finding the Dominator in an Array; Problem; Solution; How It Works; 2-6. Math and Statistics: Finding the Minimum Number of Currency Bills Required for a Given Amount; Problem; Solution; How It Works 2-7. Math and Statistics: Finding Moving AveragesProblem; Solution; How It Works; 2-8. Math and Statistics: Finding a Cumulative Sum; Problem; Solution; How It Works; 2-9. Recursive Series and Patterns: Generating Recursive Structures by Using L-System Grammar; Problem; Solution; How It Works; 2-10. Recursive Series and Patterns Step-by-Step Growth of Algae; Problem; Solution; How It Works; 2-11. Recursive Series and Patterns: Generating Logo Commands to Draw a Koch Curve; Problem; Solution; How It Works 2-17. Collections: Finding the Larger or Smaller of Several Sequences at Each IndexProblem; Solution; How It Works; 2-18. Number Theory: Generating Armstrong Numbers and Similar Number Sequences; Problem; Solution; How It Works; 2-19. Number Theory: Generating Pascal's Triangle Nonrecursively; Problem; Solution; How It Works; 2-20. Game Design: Finding All Winning Paths in an Arbitrary Tic-Tac-Toe Board; Problem; Solution; How It Works; 2-21. Series in Game Design: Solving Go Figure; Problem; Solution; How It Works 2-22. Miscellaneous Series: Finding Matching Pairs from Two Unsorted Collections |
| Altri titoli varianti | Harnessing the Power of Functional Programming in .NET Applications |
| Record Nr. | UNINA-9910300461503321 |
|
Mukherjee Sudipta
|
||
| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||