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Añadir al carritoCondición: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.
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Añadir al carritoPaperback or Softback. Condición: New. Machine Learning Concepts with Python and the Jupyter Notebook Environment: Using Tensorflow 2.0 0.99. Book.
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. 290 pages. 9.00x6.00x0.75 inches. In Stock.
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Añadir al carritoCondición: good. This book is in good condition. The cover has minor creases or bends. The binding is tight and pages are intact. Some pages may have writing or highlighting.
Publicado por Apress, Apress Sep 2020, 2020
ISBN 10: 1484259661 ISBN 13: 9781484259665
Idioma: Inglés
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware -Create, execute, modify, and share machine learning applications with Python and TensorFlow 2.0 in the Jupyter Notebook environment. This book breaks down any barriers to programming machine learning applications through the use of Jupyter Notebook instead of a text editor or a regular IDE.Yoüll start by learning how to use Jupyter Not Elektronisches Buch to improve the way you program with Python. After getting a good grounding in working with Python in Jupyter Not Elektronisches Buch, yoüll dive into what TensorFlow is, how it helps machine learning enthusiasts, and how to tackle the challenges it presents. Along the way, sample programs created using Jupyter Not Elektronisches Buch allow you to apply concepts from earlier in the book.Those who are new to machine learning can dive in with these easy programs and develop basic skills. A glossary at the end of the book provides common machine learning and Python keywords and definitions to make learning even easier.What You Will LearnProgram in Python and TensorFlowTackle basic machine learning obstaclesDevelop in the Jupyter Not Elektronisches Buch environmentWho This Book Is ForIdeal for Machine Learning and Deep Learning enthusiasts who are interested in programming with Python using Tensorflow 2.0 in the Jupyter Notebook Application. Some basic knowledge of Machine Learning concepts and Python Programming (using Python version 3) is helpful.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 320 pp. Englisch.
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Añadir al carritoPaperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 492.
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware - Create, execute, modify, and share machine learning applications with Python and TensorFlow 2.0 in the Jupyter Notebook environment.This book breaks down any barriers to programming machine learning applications through the use of Jupyter Notebook instead of a text editor or a regular IDE.You'll start by learning how to use Jupyter Not Elektronisches Buch to improve the way you program with Python. After getting a good grounding in working with Python in Jupyter Not Elektronisches Buch, you'll dive into what TensorFlow is, how it helps machine learning enthusiasts, and how to tackle the challenges it presents. Along the way, sampleprograms created using Jupyter Not Elektronisches Buch allow you to apply concepts from earlier in the book.Those who are new to machine learning can dive in with these easy programs and develop basic skills. A glossary at the end of the book provides common machine learning and Python keywords and definitions to make learning even easier. What You Will LearnProgram in Python and TensorFlowTackle basic machine learning obstaclesDevelop in the Jupyter Not Elektronisches Buch environmentWho This Book Is ForIdeal for Machine Learning and Deep Learning enthusiasts who are interested in programming with Python using Tensorflow 2.0 in the Jupyter Notebook Application. Some basic knowledge of Machine Learning concepts and Python Programming (using Python version 3) is helpful. 320 pp. Englisch.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 70,37
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Create, execute, modify, and share machine learning applications with Python and TensorFlow 2.0 in the Jupyter Notebook environment.This book breaks down any barriers to programming machine learning applications through the use of Jupyter Notebook instead of a text editor or a regular IDE.You'll start by learning how to use Jupyter Not Elektronisches Buch to improve the way you program with Python. After getting a good grounding in working with Python in Jupyter Not Elektronisches Buch, you'll dive into what TensorFlow is, how it helps machine learning enthusiasts, and how to tackle the challenges it presents. Along the way, sampleprograms created using Jupyter Not Elektronisches Buch allow you to apply concepts from earlier in the book.Those who are new to machine learning can dive in with these easy programs and develop basic skills. A glossary at the end of the book provides common machine learning and Python keywords and definitions to make learning even easier.What You Will LearnProgram in Python and TensorFlowTackle basic machine learning obstaclesDevelop in the Jupyter Not Elektronisches Buch environmentWho This Book Is ForIdeal for Machine Learning and Deep Learning enthusiasts who are interested in programming with Python using Tensorflow 2.0 in the Jupyter Notebook Application. Some basic knowledge of Machine Learning concepts and Python Programming (using Python version 3) is helpful.