Hsieh W Introduction to Environmental Data Science 2023
Category: eBooks
- views: 230
- date: 28 March 2023
- posted by: BeMyLove
Hsieh W Introduction to Environmental Data Science 2023 | 140.98 MB
English | 403 Pages
Title: Introduction to Environmental Data Science
Author: Jerry D. Davis
Year: 2023
Description:
Statistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography; pattern recognition for satellite images from remote sensing; management of agriculture and forests; assessment of climate change; and much more. With rapid advances in machine learning in the last decade, this book provides an urgently needed, comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics, with examples drawn from the environmental sciences. A broad range of topics is covered, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms and deep learning, as well as the recent merging of machine learning and physics. End‑of‑chapter exercises allow readers to develop their problem-solving skills, and online datasets allow readers to practise analysis of real data.
DOWNLOAD:
https://rapidgator.net/file/852ef95e14dd64a01cb5792c32d50ece/Hsieh_W._Introduction_to_Environmental_Data_Science_2023.rar
https://uploadgig.com/file/download/C7b64B23842a525d/Hsieh_W._Introduction_to_Environmental_Data_Science_2023.rar
Statistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography; pattern recognition for satellite images from remote sensing; management of agriculture and forests; assessment of climate change; and much more. With rapid advances in machine learning in the last decade, this book provides an urgently needed, comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics, with examples drawn from the environmental sciences. A broad range of topics is covered, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms and deep learning, as well as the recent merging of machine learning and physics. End‑of‑chapter exercises allow readers to develop their problem-solving skills, and online datasets allow readers to practise analysis of real data.
DOWNLOAD:
https://rapidgator.net/file/852ef95e14dd64a01cb5792c32d50ece/Hsieh_W._Introduction_to_Environmental_Data_Science_2023.rar
https://uploadgig.com/file/download/C7b64B23842a525d/Hsieh_W._Introduction_to_Environmental_Data_Science_2023.rar
We need your support!
Make a donation to help us stay online
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem
Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u
USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm
Information |
|||
 |
Users of GUESTS are not allowed to comment this publication. | ||