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Home > Computing and Information Technology > Computer programming / software engineering > Algorithms and data structures > Maschinelles Lernen mit Python und R für Dummies: (Für Dummies)
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Maschinelles Lernen mit Python und R für Dummies: (Für Dummies)

Maschinelles Lernen mit Python und R für Dummies: (Für Dummies)

          
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About the Book

Maschinelles Lernen ist aufregend: Mit schnellen Prozessoren und großen Speichern können Computer aus Erfahrungen lernen, künstliche Intelligenz kommt wieder in Reichweite. Mit diesem Buch verstehen Sie, was maschinelles Lernen bedeutet, für welche Probleme es sich eignet, welche neuen Herangehensweisen damit möglich sind und wie Sie mit Python, R und speziellen Werkzeugen maschinelles Lernen implementieren. Sie brauchen dafür keine jahrelange Erfahrung als Programmierer und kein Mathematikstudium. Die praktische Anwendung maschinellen Lernens steht in diesem Buch im Vordergrund. Spielen Sie mit den Tools und haben Sie Spaß dabei! Lernen Sie Fakten und Mythen zum maschinellen Lernen zu unterscheiden.

Table of Contents:

Über die Autoren 13

Einführung 25

Teil I: Einführung in das maschinelle Lernen 29

Kapitel 1: Künstliche Intelligenz in Fiktion und Realität 31

Kapitel 2: Lernen im Zeitalter von Big Data 43

Kapitel 3: Ein Ausblick auf die Zukunft 53

Teil II: Einrichtung Ihrer Programmierumgebung 63

Kapitel 4: Installation einer R-Distribution 65

Kapitel 5: Programmierung mit R und RStudio 83

Kapitel 6: Installation einer Python-Distribution 107

Kapitel 7: Programmierung mit Python und Anaconda 127

Kapitel 8: Weitere Softwareprogramme für maschinelles Lernen 151

Teil III: Mathematische Grundlagen                                 159

Kapitel 9: Mathematische Grundlagen des maschinellen Lernens  161

Kapitel 10: Fehlerfunktionen und ihre Minimierung 179

Kapitel 11: Validierung von maschinellem Lernen 191

Kapitel 12: Einfache Lerner 209

Teil IV: Aufbereitung und Verwendung von Daten

zum Lernen 225

Kapitel 13: Vorverarbeitung von Daten  227

Kapitel 14: Ausnutzung von Ähnlichkeiten in Daten 245

Kapitel 15: Einfache Anwendung von linearen Modellen 265

Kapitel 16: Komplexere Lernverfahren und neuronale Netze  287

Kapitel 17: Support Vector Machines und Kernel-Funktionen  303

Kapitel 18: Kombination von Lernalgorithmen in Ensembles 321

Teil V: Praktische Anwendung von maschinellem Lernen 337

Kapitel 19: Klassifikation von Bildern  339

Kapitel 20: Bewertung von Meinungen und Stimmungslagen 353

Kapitel 21: Produkt- und Filmempfehlungen 373

Teil VI: Der Top-Ten-Teil  387

Kapitel 22: Zehn wichtige Pakete für maschinelles Lernen 389

Kapitel 23: Zehn Methoden zur Verbesserung Ihrer maschinellen Lernmodelle 395

Stichwortverzeichnis 403

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Product Details
  • ISBN-13: 9783527713639
  • Publisher: Wiley-VCH Verlag GmbH
  • Publisher Imprint: Blackwell Verlag GmbH
  • Height: 240 mm
  • No of Pages: 405
  • Series Title: Für Dummies
  • Weight: 709 gr
  • ISBN-10: 3527713638
  • Publisher Date: 13 Sep 2017
  • Binding: Paperback
  • Language: German
  • Returnable: N
  • Spine Width: 23 mm
  • Width: 176 mm

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