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Bibliographic Details

Title: Deep Learning illustriert: Eine anschauliche Einf�hrung in Machine Vision, Natural Language Processing und Bilderzeugung f�r Programmierer und Datenanalysten
Language: German
ISBN 10: 3864906636
ISBN 13: 9783864906633
Binding: Soft cover
Condition: very good

About this title

Synopsis

Understand and use deep learning

Introduction to Artificial Intelligence, Machine Learning and Neural Networks
With many vivid illustrations and code examples to download (Jupyter notebooks)
Includes presentation of Tensor Flow/Keras and PyTorch libraries

Authors John Krohn, Grant Beylefeld, and Agla� Bassens offer you a unique visual, intuitive, and understandable introduction to techniques and applications of deep learning. With the colourful illustrations and catchy explanations of Deep Learning Illustrated, you can easily access deep learning models, and even bring you fun learning.

The first part of the book explains the basics of deep learning--why it has become so ubiquitous and how it interacts with concepts and terminologies such as artificial intelligence, machine learning, or artificial neural networks. The authors use understandable analogies, vivid graphics, and many examples.

On this basis, the authors present a practical reference and tutorial on applying proven techniques of deep learning. The essential theory is treated with as little math as possible and illuminated with Python code. Practical examples to try, available online free (Jupyter notebooks), make the theory comprehensive. This will give you a pragmatic understanding of all major deep learning approaches and their applications: Machine Vision, Natural Language Processing, Image Creation, and Game Algorithms.

To help you achieve more in less time, the authors introduce several of today's most widely used and innovative deep learning libraries, including:

TensorFlow and its high-level API, Keras
PyTorch
High-level coach, a TensorFlow API that abstracts the complexities typically associated with developing deep reinforcement learning algorithms.

"About this title" may belong to another edition of this title.

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