Deep Learning In Science
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Deep Learning In Science
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Author : Pierre Baldi
language : en
Publisher: Cambridge University Press
Release Date : 2021-07
Deep Learning In Science written by Pierre Baldi and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07 with Computers categories.
Rigorous treatment of the theory of deep learning from first principles, with applications to beautiful problems in the natural sciences.
Deep Learning For The Life Sciences
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Author : Bharath Ramsundar
language : en
Publisher: O'Reilly Media
Release Date : 2019-04-10
Deep Learning For The Life Sciences written by Bharath Ramsundar and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-10 with Science categories.
Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working
Deep Learning For Physics Research
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Author : Martin Erdmann
language : en
Publisher: World Scientific
Release Date : 2021-06-25
Deep Learning For Physics Research written by Martin Erdmann and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-25 with Science categories.
A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.
Deep Learning For Physical Scientists
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Author : Edward O. Pyzer-Knapp
language : en
Publisher: John Wiley & Sons
Release Date : 2021-09-20
Deep Learning For Physical Scientists written by Edward O. Pyzer-Knapp and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-20 with Science categories.
Discover the power of machine learning in the physical sciences with this one-stop resource from a leading voice in the field Deep Learning for Physical Scientists: Accelerating Research with Machine Learning delivers an insightful analysis of the transformative techniques being used in deep learning within the physical sciences. The book offers readers the ability to understand, select, and apply the best deep learning techniques for their individual research problem and interpret the outcome. Designed to teach researchers to think in useful new ways about how to achieve results in their research, the book provides scientists with new avenues to attack problems and avoid common pitfalls and problems. Practical case studies and problems are presented, giving readers an opportunity to put what they have learned into practice, with exemplar coding approaches provided to assist the reader. From modelling basics to feed-forward networks, the book offers a broad cross-section of machine learning techniques to improve physical science research. Readers will also enjoy: A thorough introduction to the basic classification and regression with perceptrons An exploration of training algorithms, including back propagation and stochastic gradient descent and the parallelization of training An examination of multi-layer perceptrons for learning from descriptors and de-noising data Discussions of recurrent neural networks for learning from sequences and convolutional neural networks for learning from images A treatment of Bayesian optimization for tuning deep learning architectures Perfect for academic and industrial research professionals in the physical sciences, Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access. Perfect for academic and industrial research professionals in the physical sciences, em style="font-family: Calibri, sans-serif; font-size: 11pt;"Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access. This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including: •Basic classification and regression with perceptrons •Training algorithms, such as back propagation and stochastic gradient descent and the parallelization of training •Multi-Layer Perceptrons for learning from descriptors, and de-noising data •Recurrent neural networks for learning from sequences •Convolutional neural networks for learning from images •Bayesian optimization for tuning deep learning architectures Each of these areas has direct application to physical science research, and by the end of the book, the reader should feel comfortable enough to select the methodology which is best for their situation, and be able to implement and interpret outcome of the deep learning model. The book is designed to teach researchers to think in new ways, providing them with new avenues to attack problems, and avoid roadblocks within their research. This is achieved through the inclusion of case-study like problems at the end of each chapter, which will give the reader a chance to practice what they have just learnt in a close-to-real-world setting, with example ‘solutions’ provided through an online resource. Market Description This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including: • Basic classification and regression with perceptrons • Training algorithms, such as back propagation and stochastic gradient descent and the parallelization of training • Multi-Layer Perceptrons for learning from descriptors, and de-noising data • Recurrent neural networks for learning from sequences • Convolutional neural networks for learning from images • Bayesian optimization for tuning deep learning architectures Each of these areas has direct application to physical science research, and by the end of the book, the reader should feel comfortable enough to select the methodology which is best for their situation, and be able to implement and interpret outcome of the deep learning model. The book is designed to teach researchers to think in new ways, providing them with new avenues to attack problems, and avoid roadblocks within their research. This is achieved through the inclusion of case-study like problems at the end of each chapter, which will give the reader a chance to practice what they have just learnt in a close-to-real-world setting, with example ‘solutions’ provided through an online resource.
Machine Learning And Deep Learning Techniques For Medical Science
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Author : K. Gayathri Devi
language : en
Publisher: CRC Press
Release Date : 2022-05-11
Machine Learning And Deep Learning Techniques For Medical Science written by K. Gayathri Devi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-11 with Technology & Engineering categories.
The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the integration of machine learning (ML) and deep learning (DL) algorithms that can be applied in the healthcare sector to reduce the time required by doctors, radiologists, and other medical professionals for analyzing, predicting, and diagnosing the conditions with accurate results. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis. The contributors explore the recent trends, innovations, challenges, and solutions, as well as case studies of the applications of ML and DL in intelligent system-based disease diagnosis. The chapters also highlight the basics and the need for applying mathematical aspects with reference to the development of new medical models. Authors also explore ML and DL in relation to artificial intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, and pattern recognition approaches to functional magnetic resonance imaging images. This book is for students and researchers of computer science and engineering, electronics and communication engineering, and information technology; for biomedical engineering researchers, academicians, and educators; and for students and professionals in other areas of the healthcare sector. Presents key aspects in the development and the implementation of ML and DL approaches toward developing prediction tools, models, and improving medical diagnosis Discusses the recent trends, innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis Examines DL theories, models, and tools to enhance health information systems Explores ML and DL in relation to AI prediction tools, discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities Dr. K. Gayathri Devi is a Professor at the Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Tamil Nadu, India. Dr. Kishore Balasubramanian is an Assistant Professor (Senior Scale) at the Department of EEE at Dr. Mahalingam College of Engineering & Technology, Tamil Nadu, India. Dr. Le Anh Ngoc is a Director of Swinburne Innovation Space and Professor in Swinburne University of Technology (Vietnam).
Deep Learning For Hydrometeorology And Environmental Science
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Author : Taesam Lee
language : en
Publisher: Springer Nature
Release Date : 2021-01-27
Deep Learning For Hydrometeorology And Environmental Science written by Taesam Lee and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-27 with Science categories.
This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.
Deep Learning For The Earth Sciences
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Author : Gustau Camps-Valls
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-18
Deep Learning For The Earth Sciences written by Gustau Camps-Valls and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-18 with Technology & Engineering categories.
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.
Data Scientist Pocket Guide
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Author : Mohamed Sabri
language : en
Publisher: BPB Publications
Release Date : 2021-06-24
Data Scientist Pocket Guide written by Mohamed Sabri and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-24 with Computers categories.
Discover one of the most complete dictionaries in data science. KEY FEATURES ● Simplified understanding of complex concepts, terms, terminologies, and techniques. ● Combined glossary of machine learning, mathematics, and statistics. ● Chronologically arranged A-Z keywords with brief description. DESCRIPTION This pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms is explained in the simplest words possible. This pocket guide will help you to stay up to date of the most essential terms and references used in the process of data analysis and machine learning. WHAT YOU WILL LEARN ● Get absolute clarity on every concept, process, and algorithm used in the process of data science operations. ● Keep yourself technically strong and sound-minded during data science meetings. ● Strengthen your knowledge in the field of Big data and business intelligence. WHO THIS BOOK IS FOR This book is for data professionals, data scientists, students, or those who are new to the field who wish to stay on top of industry jargon and terminologies used in the field of data science. TABLE OF CONTENTS 1. Chapter one: A 2. Chapter two: B 3. Chapter three: C 4. Chapter four: D 5. Chapter five: E 6. Chapter six: F 7. Chapter seven: G 8. Chapter eight: H 9. Chapter nine: I 10. Chapter ten: J 11. Chapter 11: K 12. Chapter 12: L 13. Chapter 13: M 14. Chapter 14: N 15. Chapter 15: O 16. Chapter 16: P 17. Chapter 17: Q 18. Chapter 18: R 19. Chapter 19 : S 20. Chapter 20 : T 21. Chapter 21 : U 22. Chapter 22 : V 23. Chapter 23: W 24. Chapter 24: X 25. Chapter 25: Y 26. Chapter 26 : Z
Deep Learning Made Easy With R
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Author : N. D. Lewis
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-01-10
Deep Learning Made Easy With R written by N. D. Lewis and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-10 with categories.
Master Deep Learning with this fun, practical, hands on guide. With the explosion of big data deep learning is now on the radar. Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teams. Other large corporations are quickly building out their own teams. If you want to join the ranks of today's top data scientists take advantage of this valuable book. It will help you get started. It reveals how deep learning models work, and takes you under the hood with an easy to follow process showing you how to build them faster than you imagined possible using the powerful, free R predictive analytics package. Bestselling decision scientist Dr. N.D Lewis shows you the shortcut up the steep steps to the very top. It's easier than you think. Through a simple to follow process you will learn how to build the most successful deep learning models used for learning from data. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful applications. If you want to accelerate your progress, discover the best in deep learning and act on what you have learned, this book is the place to get started. YOU'LL LEARN HOW TO: Understand Deep Neural Networks Use Autoencoders Unleash the power of Stacked Autoencoders Leverage the Restricted Boltzmann Machine Develop Recurrent Neural Networks Master Deep Belief Networks Everything you need to get started is contained within this book. It is your detailed, practical, tactical hands on guide - the ultimate cheat sheet for deep learning mastery. A book for everyone interested in machine learning, predictive analytic techniques, neural networks and decision science. Start building smarter models today using R! Buy the book today. Your next big breakthrough using deep learning is only a page away!
Introduction To Deep Learning
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Author : Sandro Skansi
language : en
Publisher: Springer
Release Date : 2018-02-04
Introduction To Deep Learning written by Sandro Skansi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-04 with Computers categories.
This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.