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Sprache: Englisch
Verlag: Packt Publishing Limited, GB, 2019
ISBN 10: 1789138906 ISBN 13: 9781789138900
Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes Königreich
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In den WarenkorbPaperback. Zustand: New. This book contains practical implementations of several deep learning projects in multiple domains, including in regression-based tasks such as taxi fare prediction in New York City, image classification of cats and dogs using a convolutional neural network, implementing a facial recognition security system using Siamese Neural Networks, and more.
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Sprache: Englisch
Verlag: Packt Publishing 2019-02-28, 2019
ISBN 10: 1789138906 ISBN 13: 9781789138900
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Sprache: Englisch
Verlag: Packt Publishing Limited, GB, 2019
ISBN 10: 1789138906 ISBN 13: 9781789138900
Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
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In den WarenkorbPaperback. Zustand: New. This book contains practical implementations of several deep learning projects in multiple domains, including in regression-based tasks such as taxi fare prediction in New York City, image classification of cats and dogs using a convolutional neural network, implementing a facial recognition security system using Siamese Neural Networks, and more.
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In den WarenkorbZustand: New. Print on Demand pp. 308.
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In den WarenkorbZustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book contains practical implementations of several deep learning projects in multiple domains, including in regression-based tasks such as taxi fare prediction in New York City, image classification of cats and dogs using a convolutional neural network.
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Key Features Discover neural network architectures (like CNN and LSTM) that are driving recent advancements in AI Build expert neural networks in Python using popular libraries such as Keras Includes projects such as object detection, face identification, sentiment analysis, and more Book Description Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio. What you will learn Learn various neural network architectures and its advancements in AI Master deep learning in Python by building and training neural network Master neural networks for regression and classification Discover convolutional neural networks for image recognition Learn sentiment analysis on textual data using Long Short-Term Memory Build and train a highly accurate facial recognition security system.
Taschenbuch. Zustand: Neu. Neural Network Projects with Python | The ultimate guide to using Python to explore the true power of neural networks through six projects | James Loy | Taschenbuch | Kartoniert / Broschiert | Englisch | 2019 | Packt Publishing | EAN 9781789138900 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.