Verlag: Packt Publishing, United Kingdom, 2018
ISBN 10: 1789130336 ISBN 13: 9781789130331
Sprache: Englisch
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Paperback. Zustand: Very Good+. First Edition. 259 pages; Graphs. B&W illustrations. Very minor smudge on title page. Very Good condition otherwise. No other noteworthy defects. No markings. ; - Your satisfaction is our priority. We offer free returns and respond promptly to all inquiries. Your item will be carefully cushioned in bubble wrap and securely boxed. All orders ship on the same or next business day. Buy with confidence.
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Verlag: Packt Publishing 8/29/2018, 2018
ISBN 10: 1789130336 ISBN 13: 9781789130331
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Paperback or Softback. Zustand: New. Hands-on Convolutional Neural Networks with Tensorflow. Book.
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Verlag: Packt Publishing Limited, GB, 2018
ISBN 10: 1789130336 ISBN 13: 9781789130331
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: New. Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges.Key FeaturesLearn the fundamentals of Convolutional Neural NetworksHarness Python and Tensorflow to train CNNsBuild scalable deep learning models that can process millions of itemsBook DescriptionConvolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time!We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation.After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks.Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. What you will learnTrain machine learning models with TensorFlowCreate systems that can evolve and scale during their life cycleUse CNNs in image recognition and classificationUse TensorFlow for building deep learning models Train popular deep learning modelsFine-tune a neural network to improve the quality of results with transfer learningBuild TensorFlow models that can scale to large datasets and systemsWho this book is forThis book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help.
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In den WarenkorbZustand: New. Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python l.
Verlag: Packt Publishing Limited, GB, 2018
ISBN 10: 1789130336 ISBN 13: 9781789130331
Sprache: Englisch
Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
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In den WarenkorbPaperback. Zustand: New. Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges.Key FeaturesLearn the fundamentals of Convolutional Neural NetworksHarness Python and Tensorflow to train CNNsBuild scalable deep learning models that can process millions of itemsBook DescriptionConvolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time!We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation.After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks.Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. What you will learnTrain machine learning models with TensorFlowCreate systems that can evolve and scale during their life cycleUse CNNs in image recognition and classificationUse TensorFlow for building deep learning models Train popular deep learning modelsFine-tune a neural network to improve the quality of results with transfer learningBuild TensorFlow models that can scale to large datasets and systemsWho this book is forThis book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help.
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Taschenbuch. Zustand: Neu. Hands-on Convolutional Neural Networks with Tensorflow | Iffat Zafar (u. a.) | Taschenbuch | Englisch | 2018 | Packt Publishing | EAN 9781789130331 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.