Maschinelles Lernen mit Python und R für Dummies
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Maschinelles Lernen mit Python und R für Dummies

Maschinelles Lernen mit Python und R 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
  • Returnable: N
  • 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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