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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 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 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 with Hadoop

the challenges of implementing distributed deep learning with Hadoop and how to overcome it * Implement Convolutional Neural Network (CNN;

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Hands-On Convolutional Neural Networks with TensorFlow

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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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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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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Neural Information Processing

follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred;

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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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Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and;

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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 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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Neural Information Processing

; convolutional neural networks; deep neural networks; feature learning and representation; human centred computing; human centred computing and medicine;

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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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Hardware Architectures for Deep Learning

, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for;

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Practical Deep Learning

concepts such as classes and labels, building a dataset, and what a model is and does before presenting classic machine learning models, neural;

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Deep Learning with R Cookbook

intricacies of R deep learning packages to perform a range of deep learning tasks Implement deep learning techniques and algorithms for real-world use;

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Practical Convolutional Neural Networks

, Instance Segmentation, GANs and more Implement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent;

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Learning Deep Learning

the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural;

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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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Practical TensorFlow.js

learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural;

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Practical TensorFlow.js

learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks;

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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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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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