Haskell data analysis cookbook : explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes /

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Step-by-step recipes filled with practical code samples and engaging examples demonstrate Haskell in practice, and then the concepts behind the code. This book shows functional developers and analysts how to leverage their existing knowledge of Haskell specifically for high-quality data analysis. A...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Shukla, Nishant, 1992- (Autor)
Otros Autores: Blaminsky, Jarek (Diseñador de portada)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham [England] : Packt Publishing, 2014.
Colección:Quick answers to common problems.
Temas:
Haskell (Computer program language)
Haskell (Langage de programmation)
COMPUTERS > Programming Languages > General.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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100 1 |a Shukla, Nishant,  |d 1992-  |e author. 
245 1 0 |a Haskell data analysis cookbook :  |b explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes /  |c Nishant Shukla ; cover image by Jarek Blaminsky. 
264 1 |a Birmingham [England] :  |b Packt Publishing,  |c 2014. 
264 4 |c ©2014 
300 |a 1 online resource (334 pages) :  |b illustrations (some color). 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
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490 1 |a Quick answers to common problems 
588 0 |a Online resource; title from PDF title page (ebrary, viewed July 10, 2014). 
500 |a Includes index. 
520 |a Step-by-step recipes filled with practical code samples and engaging examples demonstrate Haskell in practice, and then the concepts behind the code. This book shows functional developers and analysts how to leverage their existing knowledge of Haskell specifically for high-quality data analysis. A good understanding of data sets and functional programming is assumed. 
505 0 |a Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: The Hunt for Data; Introduction; Harnessing data from various sources; Accumulating text data from a file path; Catching I/O code faults; Keeping and representing data from a CSV file; Examining a JSON file with the aeson package; Reading an XML file using the HXT package; Capturing table rows from an HTML page; Understanding how to perform HTTP GET requests; Learning how to perform HTTP POST requests; Traversing online directories for data 
505 8 |a Using MongoDB queries in HaskellReading from a remote MongoDB server; Exploring data from a SQLite database; Chapter 2: Integrity and Inspection; Introduction; Trimming excess whitespace; Ignoring punctuation and specific characters; Coping with unexpected or missing input; Validating records by matching regular expressions; Lexing and parsing an e-mail address; Deduplication of nonconflicting data items; Deduplication of conflicting data items; Implementing a frequency table using Data.List; Implementing a frequency table using Data.MultiSet; Computing the Manhattan distance 
505 8 |a Computing the Euclidean distanceComparing scaled data using the Pearson correlation coefficient; Comparing sparse data using cosine similarity; Chapter 3: The Science of Words; Introduction; Displaying a number in another base; Reading a number from another base; Searching for a substring using Data.ByteString; Searching a string using the Boyer-Moore-Horspool algorithm; Searching a string using the Rabin-Karp algorithm; Splitting a string on lines, words, or arbitrary tokens; Finding the longest common subsequence; Computing a phonetic code; Computing the edit distance 
505 8 |a Computing the Jaro-Winkler distance between two stringsFinding strings within one-edit distance; Fixing spelling mistakes; Chapter 4: Data Hashing; Introduction; Hashing a primitive data type; Hashing a custom data type; Running popular cryptographic hash functions; Running a cryptographic checksum on a file; Performing fast comparisons between data types; Using a high-performance hash table; Using Google's CityHash hash functions for strings; Computing a Geohash for location coordinates; Using a bloom filter to remove unique items; Running MurmurHash, a simple but speedy hashing algorithm 
505 8 |a Measuring image similarity with perceptual hashesChapter 5: The Dance with Trees; Introduction; Defining a binary tree data type; Defining a rose tree (multiway tree) data type; Traversing a tree depth-first; Traversing a tree breadth-first; Implementing a Foldable instance for a tree; Calculating the height of a tree; Implementing a binary search tree data structure; Verifying the order property of a binary search tree; Using a self-balancing tree; Implementing a min-heap data structure; Encoding a string using a Huffman tree; Decoding a Huffman code; Chapter 6: Graph Fundamentals 
546 |a English. 
590 |a eBooks on EBSCOhost  |b EBSCO eBook Subscription Academic Collection - Worldwide 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Haskell (Computer program language) 
650 6 |a Haskell (Langage de programmation) 
650 7 |a COMPUTERS  |x Programming Languages  |x General.  |2 bisacsh 
650 7 |a Haskell (Computer program language)  |2 fast  |0 (OCoLC)fst00951830 
700 1 |a Blaminsky, Jarek,  |e cover designer. 
776 0 8 |i Print version:  |a Shukla, Nishant.  |t Haskell data analysis cookbook : explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes.  |d Birmingham, [England] : Packt Publishing, ©2014  |h v, 315 pages  |z 9781783286331 
830 0 |a Quick answers to common problems. 
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