For High Performance Computing, Deep Neural Networks and Data Science

Principles of High-Performance Processor

📁 Principles of High-Performance Processor Design: For High Performance Computing, Deep Neural Networks and Data Science

✍ Junichiro Makino 📂 Library 📅 2021 🏛 Springer 🌐 English

<div>This book describes how we can design and make efficient processors for high-performance computing, AI, and data science. Although there are many textbooks on the design of processors we do not have a widely accepted definition of the efficiency of a general-purpose computer architecture. Witho

Deep Learning and Convolutional Neural N

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

✍ Le Lu; Yefeng Zheng; Gustavo Carneiro; Lin Yang 📂 Library 📅 2017 🏛 Springer 🌐 English

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the th

Deep Learning and Convolutional Neural N

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

✍ Le Lu, Yefeng Zheng, Gustavo Carneiro, Lin Yang (eds.) 📂 Library 📅 2017 🏛 Springer International Publishing 🌐 English

High-Performance Computing of Big Data f

📁 High-Performance Computing of Big Data for Turbulence and Combustion

✍ Sergio Pirozzoli, Tapan K. Sengupta 📂 Library 📅 2019 🏛 Springer International Publishing 🌐 English

<p>This book provides state-of-art information on high-accuracy scientific computing and its future prospects, as applicable to the broad areas of fluid mechanics and combustion, and across all speed regimes. Beginning with the concepts of space-time discretization and dispersion relation in numeric

Computational neural networks for geophy

📁 Computational neural networks for geophysical data processing

✍ Mary M. Poulton (Eds.) 📂 Library 📅 2001 🏛 Pergamon

This book was primarily written for an audience that has heard about neural networks or has had some experience with the algorithms, but would like to gain a deeper understanding of the fundamental material. For those that already have a solid grasp of how to create a neural network application, th

Computational Neural Networks for Geophy

📁 Computational Neural Networks for Geophysical Data Processing

✍ Poulton M.M. 📂 Library 📅 2001 🌐 English

This book was primarily written for an audience that has heard about neural networks or has had some experience with the algorithms, but would like to gain a deeper understanding of the fundamental material. For those that already have a solid grasp of how to create a neural network application, thi