Grundlagen der Mathematik für KI
Grundlagen der Mathematik für KI

Grundlagen der Mathematik für KI


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About the Book

Buchbeschreibung

Tauchen Sie ein in die faszinierende Welt der Mathematik für Künstliche Intelligenz (KI) und erschließen Sie deren praktische Anwendungen mit Python. Dieses umfassende Werk bietet eine systematische Darlegung wesentlicher mathematischer Konzepte, die von Grundlagen bis zu fortgeschrittenen Techniken reichen. Jede Theorie wird durch praxisnahes Programmieren mit Python ergänzt, was Ihnen erlaubt, Ihre Kenntnisse sofort anzuwenden und zu vertiefen. Egal, ob Sie ein Student, ein Fachmann oder einfach ein neugieriger Lernender sind, dieses Buch wird Ihnen helfen, ein tieferes Verständnis der Mathematik zu erlangen, die modernen KI-Technologien zugrunde liegt.

Hauptmerkmale

- Umfassende Einführung in mathematische Konzepte für KI.
- Schritt-für-Schritt-Anleitungen zur Implementierung mit Python.
- Anwendungsorientierte Beispiele zur sofortigen Umsetzung.
- Klar strukturierte Kapitel, die sowohl Anfänger als auch Fortgeschrittene bedienen.
- Praktische Übungen zur Festigung des Gelernten.

Was Sie lernen werden

- Einführung in Vektoren und Matrizen für AI-Berechnungen.
- Untersuchung von Kreuz- und Skalarprodukten zur Vektortransformation.
- Analyse von Eigenwerten und Eigenvektoren und deren Anwendung in AI.
- Vertiefung in die Singulärwertzerlegung für die Datenreduktion.
- Diagonalisierung von Matrizen und deren Bedeutung in der Künstlichen Intelligenz.
- Detaillierte Untersuchung der Vektorraumstruktur.
- Umfassender Einblick in den Gradientenabstieg zur Optimierung.
- Konvexe Optimierung und ihre Rolle in maschinellem Lernen.
- Einsatz von Lagrange-Multiplikatoren bei Optimierungsproblemen.
- Grundlagen der stochastischen Optimierung in KI.
- Einführung in Zufallsvariablen und ihre Verteilungen.
- Bedeutende Erkenntnisse über die Normalverteilung in statistischen Modellen.
- Anwendung der Bayesschen Statistik auf KI-Modelle.
- Einsatz von Markov-Ketten für zeitbasierte KI-Modelle.
- Grundlegendes Verständnis von Entropie und Informationstheorie.
- Unterschiedsbewertung mit der Kullback-Leibler-Divergenz.
- Mechanismen von Neuronen und Aktivierungsfunktionen.
- Verständliche Darstellung der Backpropagation-Algorithmen.
- Untersuchung der Geometrie von konvexen Hüllen.
- Einsatz von Support Vector Machines im Klassifikationskontext.
- Anwendung von Kernel-Tricks zur Datenverarbeitung.
- Grundlagen der Regression und Verlustoptimierung.
- Bedeutung der linearen Regression in der Datenanalyse.
- Logistische Regression für binäre Klassifikationen.
- Regularisierung zur Vermeidung von Overfitting.
- Einsatz der Fouriertransformation in der Signalverarbeitung.
- Untersuchung der Wavelet-Transformationen für lokale Signalvariationen.
- Grundlagen der Graphentheorie für Netzwerkdaten.
- Optimierung des Datenflusses durch Netzwerkflusstheorie.
- Einführung in Graphen-Fouriertransformationen zur Frequenzanalyse.
- Bedeutung von Tensoren in KI-Computationen.
- Analyse der Kreuzentropieverlustfunktion für Vorhersagemodelle.


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Product Details
  • ISBN-13: 9798340501981
  • Publisher: Amazon Digital Services LLC - Kdp
  • Binding: Paperback
  • Language: German
  • Returnable: N
  • Sub Title: Praktische Anwendung mit Python
  • Width: 152 mm
  • ISBN-10: 8340501984
  • Publisher Date: 27 Sep 2024
  • Height: 229 mm
  • No of Pages: 356
  • Spine Width: 19 mm
  • Weight: 526 gr


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