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in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural;
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in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural;
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formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured;
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formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured;
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guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good;
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guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good;
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AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks;
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AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks;
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follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred;
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learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural;
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Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or;
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learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks;
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of Convolutional Neural Networks Harness Python and Tensorflow to train CNNs Build scalable deep learning models that can process millions of items Book;
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With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many;
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images Prepare your dataset for machine learning Create classical, convolutional, and deep neural networks Create a RESTful image classification;
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deep learning in clinical applicationsIntroduces several techniques for medical image processing and analysis for CAD systems design;
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models Understand DL techniques, such as meta-learning and graph neural networks Who this book is forThis book is for data scientists, deep;
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