Machine Learning mit Python und Keras, TensorFlow 2 und Scikit-learn | Das umfassende Praxis-Handbuch f�r Data Science, Deep Learning und Predictive Analytics

Editor

MITP Verlags GmbH

Fecha de publicaci�n

2021

Idioma

Alem�n

ISBN 10

374750213X

ISBN 13

9783747502136

Encuadernaci�n

Taschenbuch

Estado

Neu

Dimensiones

Peso

1264 gramos

Cat�logos de vendedores

B�cher

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Sinopsis

Datenanalyse mit ausgereiften statistischen Modellen des Machine Learnings
Anwendung der wichtigsten Algorithmen und Python-Bibliotheken wie NumPy, SciPy, Scikit-learn, Keras, TensorFlow 2, Pandas und Matplotlib
Best Practices zur Optimierung Ihrer Machine-Learning-Algorithmen

Mit diesem Buch erhalten Sie eine umfassende Einf�hrung in die Grundlagen und den effektiven Einsatz von Machine-Learning- und Deep-Learning-Algorithmen und wenden diese anhand zahlreicher Beispiele praktisch an. Daf�r setzen Sie ein breites Spektrum leistungsf�higer Python-Bibliotheken ein, insbesondere Keras, TensorFlow 2 und Scikit-learn. Auch die f�r die praktische Anwendung unverzichtbaren mathematischen Konzepte werden verst�ndlich und anhand zahlreicher Diagramme anschaulich erl�utert.

Die dritte Auflage dieses Buchs wurde f�r TensorFlow 2 komplett aktualisiert und ber�cksichtigt die j�ngsten Entwicklungen und Technologien, die f�r Machine Learning, Neuronale Netze und Deep Learning wichtig sind. Dazu z�hlen insbesondere die neuen Features der Keras-API, das Synthetisieren neuer Daten mit Generative Adversarial Networks (GANs) sowie die Entscheidungsfindung per Reinforcement Learning.

Ein sicherer Umgang mit Python wird vorausgesetzt.

Aus dem Inhalt:

Trainieren von Lernalgorithmen und Implementierung in Python
G�ngige Klassifikationsalgorithmen wie Support Vector Machines (SVM), Entscheidungsb�ume und Random Forest
Natural Language Processing zur Klassifizierung von Filmbewertungen
Clusteranalyse zum Auffinden verborgener Muster und Strukturen in Ihren Daten
Deep-Learning-Verfahren f�r die Bilderkennung
Datenkomprimierung durch Dimensionsreduktion
Training Neuronaler Netze und GANs mit TensorFlow 2
Kombination verschiedener Modelle f�r das Ensemble Learning
Einbettung von Machine-Learning-Modellen in Webanwendungen
Stimmungsanalyse in Social Networks
Modellierung sequenzieller Daten durch rekurrente Neuronale Netze
Reinforcement Learning und Implementierung von Q-Learning-Algorithmen

Acerca del autor

Sebastian Raschka ist Assistant Professor f�r Statistik an der University of Wisconsin-Madison, wo er an der Entwicklung neuer Deep-Learning-Architekturen im Gebiet der Biometrie forscht. Er leitete verschiedene Seminare u.a. auf der SciPy-Konferenz.

Vahid Mirjalili erforschte mehrere Jahre an der Michigan State University Anwendungen des Machine Learnings in verschiedenen Computer-Vision-Projekten. Heute ist er in der Forschung des Unternehmens 3M t�tig, wo er moderne Machine-Learning- und Deep-Learning-Verfahren in der Praxis anwendet.

"Sobre este t�tulo" puede pertenecer a otra edici�n de este libro.

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