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Practical Deep Learning Python Based by Kneusel Ronald (26 results)
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Practical Deep Learning : A Python-Based Introduction
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Practical Deep Learning: A Python-Based Introduction
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Practical Deep Learning : A Python-based Introduction
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Practical Deep Learning : A Python-Based Introduction
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Practical Deep Learning, 2nd Edition: A Python-Based Introduction
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Practical Deep Learning : A Python-based Introduction
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Taschenbuch. Condition: Neu. Practical Deep Learning | A Python-Based Introduction | Ronald T. Kneusel | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2021 | Random House LLC US | EAN 9781718500747 | Verantwortliche Person für die EU: Springer Fachmedien Wiesbaden GmbH, Postfach:15 46, 65189 Wiesbaden, info[at]bod[dot]de | Anbieter: preigu.
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Practical Deep Learning : A Python-Based Introduction
Language: English
Published by Random House LLC US Feb 2021, 2021
ISBN 10: 1718500742 ISBN 13: 9781718500747
Taschenbuch. Condition: Neu. Neuware - Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects.If you ve been curious about artificial intelligence and machine learning but didn t know where to start, this is the book you ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further.All you need is basic familiarity with computer programming and high school math the book will cover the rest. After an introduction to Python, you ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models performance.You ll also learn:How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector MachinesHow neural networks work and how they re trainedHow to use convolutional neural networksHow to develop a successful deep learning model from scratchYou ll conduct experiments along the way, building to a final case study that incorporates everything you ve learned.The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.
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Taschenbuch. Condition: Neu. Practical Deep Learning | A Python-Based Introduction | Ronald T. Kneusel | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | Random House LLC US | EAN 9781718504202 | Verantwortliche Person für die EU: Springer Fachmedien Wiesbaden GmbH, Postfach:15 46, 65189 Wiesbaden, info[at]bod[dot]de | Anbieter: preigu.
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Practical Deep Learning : A Python-Based Introduction
Language: English
Published by Random House LLC US Jul 2025, 2025
ISBN 10: 1718504209 ISBN 13: 9781718504202
Taschenbuch. Condition: Neu. Neuware - If you've been curious about artificial intelligence and machine learning but didn't know where to start, this is the book you've been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning, 2nd Edition teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math - the book will cover the rest. After an introduction to Python, you'll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models' performance. You'll also learn: How to use classic machine learning models like k-Nearest Neighbours, Random Forests, and Support Vector Machines, How neural networks work and how they're trained, How to use convolutional neural networks, How to develop a successful deep learning model from scratch. You'll conduct experiments along the way, building to a final case study that incorporates everything you've learned. This second edition is thoroughly revised and updated, and adds six new chapters to further your exploration of deep learning from basic CNNs to more advanced models. New chapters cover fine tuning, transfer learning, object detection, semantic segmentation, multilabel classification, self-supervised learning, generative adversarial networks, and large language models. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning, 2nd Edition will give you the skills and confidence to dive into your own machine learning projects.
