Praxiseinstieg Machine Learning mit Scikit-Learn, Keras und TensorFlow: Konzepte, Tools und Techniken für intelligente Systeme (Aktuell zu TensorFlow 2) (Animals)

Géron, Aurélien

ISBN 10: 3960091249 / ISBN 13: 9783960091240

Published by Dpunkt.Verlag GmbH, 2020

About this Item

Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.

Seller Inventory # M03960091249-G

Bibliographic Details

• Title: Praxiseinstieg Machine Learning mit Scikit-Learn, Keras und TensorFlow: Konzepte, Tools und Techniken für intelligente Systeme (Aktuell zu TensorFlow 2) (Animals)
• Publisher: Dpunkt.Verlag GmbH
• Publication Date: 2020
• Language: German
• ISBN 10: 3960091249
• ISBN 13: 9783960091240
• Binding: Soft cover
• Condition: good

About this title

Synopsis

A series of Deep Learning breakthroughs have boosted the whole field of machine learning over the last decade. Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.

This hands-on book shows you how to use:

Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry point
TensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networks
Practical code examples that you can apply without learning excessive machine learning theory or algorithm details