Artificial Intelligence For Science A Deep Learning Revolution

This book PDF is perfect for those who love Computers genre, written by Alok Choudhary and published by World Scientific which was released on 21 March 2023 with total hardcover pages 803. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Artificial Intelligence For Science A Deep Learning Revolution books below.

Artificial Intelligence For Science A Deep Learning Revolution
Artificial Intelligence For Science A Deep Learning Revolution
Author : Alok Choudhary
File Size : 40,6 Mb
Publisher : World Scientific
Language : English
Release Date : 21 March 2023
ISBN : 9789811265686
Pages : 803 pages
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Artificial Intelligence For Science A Deep Learning Revolution by Alok Choudhary Book PDF Summary

This unique collection introduces AI, Machine Learning (ML), and deep neural network technologies leading to scientific discovery from the datasets generated both by supercomputer simulation and by modern experimental facilities.Huge quantities of experimental data come from many sources — telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities such as the Large Hadron Collider (LHC) at CERN in Geneva and the ITER Tokamak in France. These sources generate many petabytes moving to exabytes of data per year. Extracting scientific insights from these data is a major challenge for scientists, for whom the latest AI developments will be essential.The timely handbook benefits professionals, researchers, academics, and students in all fields of science and engineering as well as AI, ML, and neural networks. Further, the vision evident in this book inspires all those who influence or are influenced by scientific progress.

Artificial Intelligence For Science A Deep Learning Revolution

Artificial Intelligence For Science A Deep Learning Revolution by Alok Choudhary,Geoffrey C Fox,Tony Hey

This unique collection introduces AI, Machine Learning (ML), and deep neural network technologies leading to scientific discovery from the datasets generated both by supercomputer simulation and by modern experimental facilities.Huge quantities of experimental data come from many sources — telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities

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