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Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured;

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Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured;

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Deep Learning and Convolutional Neural Networks for Medical Image Computing

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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Deep Learning and Convolutional Neural Networks for Medical Image Computing: Precision Medicine, High Performance and Large-Scale Datasets

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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Deep Learning Models for Medical Imaging

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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Introduction to Deep Learning for Healthcare

multi-modality data. Healthcare data include both structured and unstructured information. Structured data include various medical codes for;

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State of the Art in Neural Networks and Their Applications

learning approaches for the detection of renal, retinal, breast, skin, and dental abnormalities and more. Includes applications of neural;

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Convolutional Neural Networks in Visual Computing

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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Convolutional Neural Networks in Visual Computing

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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Deep Learning from Scratch Building with Python from First Principles

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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Handbook of Deep Learning in Biomedical Engineering

Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that;

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Deep Learning and Practice with MindSpore

AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks;

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Deep Learning and Practice with MindSpore

AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks;

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Tree-Based Convolutional Neural Networks

This book proposes a novel neural architecture, tree-based convolutional neural networks (TBCNNs),for processing tree-structured data;

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Learning Approaches in Signal Processing

filtering, and multirate DSP. It discusses the applications that make use of deep learning, convolutional neural networks, random forests, etc. The;

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Trends in Deep Learning Methodologies

Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep;

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Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering;

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Artificial Neural Networks and Machine Learning - ICANN 2017

; Convolutional Neural Networks; Games and Strategy; Representation and Classification; Clustering; Learning from Data Streams and Time Series; Image;

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Artificial Neural Networks and Machine Learning - ICANN 2017

; Convolutional Neural Networks; Games and Strategy; Representation and Classification; Clustering; Learning from Data Streams and Time Series; Image;

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Advanced Deep Learning with R

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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MATLAB Deep Learning

, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB;

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Deep Learning Techniques for Biomedical and Health Informatics

improve clinical decision-making Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical;

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Deep Learning for Data Analytics

analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks;

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Hands-On Deep Learning with TensorFlow

about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data;

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Deep Learning in Mining of Visual Content

some particular very popular architectures used in the domain: convolutional neural networks and recurrent neural networks. Deep Learning;

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Deep Neural Networks in a Mathematical Framework

gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks;

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Machine Learning and the Internet of Medical Things in Healthcare

Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for;

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