Physics Of Data Science And Machine Learning
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Physics Of Data Science And Machine Learning
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Author : Ijaz A. Rauf
language : en
Publisher: CRC Press
Release Date : 2021-11-28
Physics Of Data Science And Machine Learning written by Ijaz A. Rauf and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-28 with Computers categories.
Physics of Data Science and Machine Learning links fundamental concepts of physics to data science, machine learning, and artificial intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists, marrying quantum and statistical mechanics with modern data mining, data science, and machine learning. It also explains how to integrate these techniques into the design of experiments, while exploring neural networks and machine learning, building on fundamental concepts of statistical and quantum mechanics. This book is a self-learning tool for physicists looking to learn how to utilize data science and machine learning in their research. It will also be of interest to computer scientists and applied mathematicians, alongside graduate students looking to understand the basic concepts and foundations of data science, machine learning, and artificial intelligence. Although specifically written for physicists, it will also help provide non-physicists with an opportunity to understand the fundamental concepts from a physics perspective to aid in the development of new and innovative machine learning and artificial intelligence tools. Key Features: Introduces the design of experiments and digital twin concepts in simple lay terms for physicists to understand, adopt, and adapt. Free from endless derivations; instead, equations are presented and it is explained strategically why it is imperative to use them and how they will help in the task at hand. Illustrations and simple explanations help readers visualize and absorb the difficult-to-understand concepts. Ijaz A. Rauf is an adjunct professor at the School of Graduate Studies, York University, Toronto, Canada. He is also an associate researcher at Ryerson University, Toronto, Canada and president of the Eminent-Tech Corporation, Bradford, ON, Canada.
The Statistical Physics Of Data Assimilation And Machine Learning
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Author : Henry D. I. Abarbanel
language : en
Publisher: Cambridge University Press
Release Date : 2022-02-17
The Statistical Physics Of Data Assimilation And Machine Learning written by Henry D. I. Abarbanel 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 2022-02-17 with Computers categories.
The theory of data assimilation and machine learning is introduced in an accessible manner for undergraduate and graduate students.
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.
Data Engineering And Data Science
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Author : Kukatlapalli Pradeep Kumar
language : en
Publisher: John Wiley & Sons
Release Date : 2023-08-29
Data Engineering And Data Science written by Kukatlapalli Pradeep Kumar 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 2023-08-29 with Mathematics categories.
DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one-stop shop” for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.
Machine Learning For Signal Processing
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Author : Max A. Little
language : en
Publisher: Oxford University Press, USA
Release Date : 2019
Machine Learning For Signal Processing written by Max A. Little and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Computers categories.
Describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gradually so that the ideas and algorithms can be implemented in practical software applications.
Handbook On Big Data And Machine Learning In The Physical Sciences In 2 Volumes
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Author :
language : en
Publisher: World Scientific
Release Date : 2020-03-10
Handbook On Big Data And Machine Learning In The Physical Sciences In 2 Volumes written by and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-10 with Computers categories.
This compendium provides a comprehensive collection of the emergent applications of big data, machine learning, and artificial intelligence technologies to present day physical sciences ranging from materials theory and imaging to predictive synthesis and automated research. This area of research is among the most rapidly developing in the last several years in areas spanning materials science, chemistry, and condensed matter physics.Written by world renowned researchers, the compilation of two authoritative volumes provides a distinct summary of the modern advances in instrument — driven data generation and analytics, establishing the links between the big data and predictive theories, and outlining the emerging field of data and physics-driven predictive and autonomous systems.
Data Driven Science And Engineering
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Author : Steven L. Brunton
language : en
Publisher: Cambridge University Press
Release Date : 2022-05-05
Data Driven Science And Engineering written by Steven L. Brunton 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 2022-05-05 with Computers categories.
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Knowledge Guided Machine Learning
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Author : Anuj Karpatne
language : en
Publisher: CRC Press
Release Date : 2022-08-15
Knowledge Guided Machine Learning written by Anuj Karpatne and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-15 with Business & Economics categories.
Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML
Quantum Computing With Python
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Author : Jason Test
language : en
Publisher: Independently Published
Release Date : 2021-03-17
Quantum Computing With Python written by Jason Test and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-17 with categories.
*KINDLE VERSION Discounted at $ 9.99 instead of $ 14.99... Get QUANTUM PHYSICS section for FREE!! "Master the best methods for PYTHON. Learn how to programming as a pro and get positive ROI in 7 days with data science and machine learning" Are you looking for a super-fast computer programming course? Would you like to learn the Python Programming Language in 7 days? Do you want to increase your business thanks to the web applications? Finally on launch the most complete Python+Quantum Physics guide with 4 Manuscripts in 1 book! This is a challenging tool to find real help with many unique contents that indirectly will answer to your doubts: 1-Python for beginners 2-Python for Data Science 3-Python Crash Course and special and FREE section: 4-Quantum Physics for beginners QUANTUM COMPUTING WITH PYTHON will introduce you many selected practices for coding. You will discover as a beginner the world of data science, machine learning and artificial intelligence. The following list is just a tiny fraction of what you will learn in this collection bundle. 1) Python for beginners ✓ The basics of Python programming ✓ Easy-to-follow steps for reading and writing codes. ✓ 3 best strategies with NumPy, Pandas, Matplotlib 2) Python for Data science ✓3 reasons why Python is fundamental for Data Science ✓How to use Python Data Analysis in your business ✓ How to set up the Python environment for Data Science ✓Most important Machine Learning Algorithms 3) Python Crash Course ✓ A Proven Method to Write your First Program in 7 Days ✓The One Thing You Need to Debug your Codes in Python ✓5 Practical exercises to start programming 4) Quantum Physics for beginners ✓The law and principles of quantum physics and the law of attraction; ✓The power of quantum ✓Differences between Quantum cryptography and Quantum computers Examples and step-by-step guides will guide you during the code-writing learning process. The description of each topic is crystal-clear and you can easily practice with related exercises. You will also learn all the 3 best tricks of writing codes with point by point descriptions of the code elements. Even if you have never written a programming code before, you will quickly grasp the basics thanks to visual charts and guidelines for coding. If you really wish to to learn Python and master its language, please click the BUY NOW button.
On The Epistemology Of Data Science
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Author : Wolfgang Pietsch
language : en
Publisher: Springer Nature
Release Date : 2021-12-10
On The Epistemology Of Data Science written by Wolfgang Pietsch and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-10 with Philosophy categories.
This book addresses controversies concerning the epistemological foundations of data science: Is it a genuine science? Or is data science merely some inferior practice that can at best contribute to the scientific enterprise, but cannot stand on its own? The author proposes a coherent conceptual framework with which these questions can be rigorously addressed. Readers will discover a defense of inductivism and consideration of the arguments against it: an epistemology of data science more or less by definition has to be inductivist, given that data science starts with the data. As an alternative to enumerative approaches, the author endorses Federica Russo’s recent call for a variational rationale in inductive methodology. Chapters then address some of the key concepts of an inductivist methodology including causation, probability and analogy, before outlining an inductivist framework. The inductivist framework is shown to be adequate and useful for an analysis of the epistemological foundations of data science. The author points out that many aspects of the variational rationale are present in algorithms commonly used in data science. Introductions to algorithms and brief case studies of successful data science such as machine translation are included. Data science is located with reference to several crucial distinctions regarding different kinds of scientific practices, including between exploratory and theory-driven experimentation, and between phenomenological and theoretical science. Computer scientists, philosophers and data scientists of various disciplines will find this philosophical perspective and conceptual framework of great interest, especially as a starting point for further in-depth analysis of algorithms used in data science.