Fundamental Mathematical Concepts For Machine Learning In Science
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Fundamental Mathematical Concepts For Machine Learning In Science
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Author : Umberto Michelucci
language : en
Publisher: Springer Nature
Release Date : 2024-05-16
Fundamental Mathematical Concepts For Machine Learning In Science written by Umberto Michelucci and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-16 with Mathematics categories.
This book is for individuals with a scientific background who aspire to apply machine learning within various natural science disciplines—such as physics, chemistry, biology, medicine, psychology and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, maintaining rigorous mathematical integrity. For readers more versed in mathematics, the book includes advanced sections that are not prerequisites for the initial reading. It ensures concepts are clearly defined and theorems are proven where it's pertinent. Machine learning transcends the mere implementation and training of algorithms; it encompasses the broader challenges of constructing robust datasets, model validation, addressing imbalanced datasets, and fine-tuning hyperparameters. These topics are thoroughly examined within the text, along with the theoretical foundations underlying these methods. Rather than concentrating on particular algorithms this book focuses on the comprehensive concepts and theories essential for their application. It stands as an indispensable resource for any scientist keen on integrating machine learning effectively into their research. Numerous texts delve into the technical execution of machine learning algorithms, often overlooking the foundational concepts vital for fully grasping these methods. This leads to a gap in using these algorithms effectively across diverse disciplines. For instance, a firm grasp of calculus is imperative to comprehend the training processes of algorithms and neural networks, while linear algebra is essential for the application and efficient training of various algorithms, including neural networks. Absent a solid mathematical base, machine learning applications may be, at best, cursory, or at worst, fundamentally flawed. This book lays the foundation for a comprehensive understanding of machine learning algorithms and approaches.
Fundamental Concepts Of Machine Learning
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Author : Prof. Gaikwad Anil Pandurang
language : en
Publisher: Xoffencerpublication
Release Date : 2023-06-06
Fundamental Concepts Of Machine Learning written by Prof. Gaikwad Anil Pandurang and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-06 with Medical categories.
The term "machine learning" refers to a variety of computer technologies that make use of previous data in order to either enhance performance or develop more accurate predictions. The term was coined by British computer scientist Stuart Russell. The collective term for these many modes of instruction is "deep learning." In the context of this situation, the term "experience" refers to the historical knowledge that has been amassed and is now accessible to the student. This knowledge is what is supposed to be referred to as "experience." The vast majority of the time, this information is stored in the form of electronic data that may be investigated when it is necessary to do so. This data may be collected in the form of digitized human-labeled training sets, or it could be received in the form of any other kind of information that is gained by coming into touch with the environment. When it comes to determining how accurate the predictions of a learner are, the things that count the most are the kind of the object that is being anticipated as well as the quantity of that item that is being forecasted. An example of a learning challenge would be to find a way to properly predict the topic of papers that have not been read by looking at a limited number of documents that have been selected at random and tagged with themes. This might be accomplished by looking at a small number of documents that have been categorized. In this scenario, the student is challenged with coming up with a solution to the issue of how to accurately identify the topic of articles that have not yet been read. If there are more persons involved in the sample, then the task should, in principle, be simpler to finish. However, the level of difficulty of the assignment also relies on the quality of the labels that were applied to the papers in the sample. This will make the work more or less challenging. Because of this, the task might either become much simpler or significantly more challenging. This is because some of the labels could not be completely correct, and it also is depending on the number of subjects that can be accessed. The process of machine learning calls for the development of prediction algorithms that are capable of producing outcomes that are both accurate and efficient.
Essential Math For Data Science
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Author : Thomas Nield
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-05-26
Essential Math For Data Science written by Thomas Nield and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-26 with Computers categories.
Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market
Machine Learning For Developers
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Author : Rodolfo Bonnin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-10-26
Machine Learning For Developers written by Rodolfo Bonnin and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-26 with Computers categories.
Your one-stop guide to becoming a Machine Learning expert. About This Book Learn to develop efficient and intelligent applications by leveraging the power of Machine Learning A highly practical guide explaining the concepts of problem solving in the easiest possible manner Implement Machine Learning in the most practical way Who This Book Is For This book will appeal to any developer who wants to know what Machine Learning is and is keen to use Machine Learning to make their day-to-day apps fast, high performing, and accurate. Any developer who wants to enter the field of Machine Learning can effectively use this book as an entry point. What You Will Learn Learn the math and mechanics of Machine Learning via a developer-friendly approach Get to grips with widely used Machine Learning algorithms/techniques and how to use them to solve real problems Get a feel for advanced concepts, using popular programming frameworks. Prepare yourself and other developers for working in the new ubiquitous field of Machine Learning Get an overview of the most well known and powerful tools, to solve computing problems using Machine Learning. Get an intuitive and down-to-earth introduction to current Machine Learning areas, and apply these concepts on interesting and cutting-edge problems. In Detail Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the globe is, “How do I get started in Machine Learning?”. One reason could be attributed to the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This book is a systematic guide teaching you how to implement various Machine Learning techniques and their day-to-day application and development. You will start with the very basics of data and mathematical models in easy-to-follow language that you are familiar with; you will feel at home while implementing the examples. The book will introduce you to various libraries and frameworks used in the world of Machine Learning, and then, without wasting any time, you will get to the point and implement Regression, Clustering, classification, Neural networks, and more with fun examples. As you get to grips with the techniques, you'll learn to implement those concepts to solve real-world scenarios for ML applications such as image analysis, Natural Language processing, and anomaly detections of time series data. By the end of the book, you will have learned various ML techniques to develop more efficient and intelligent applications. Style and approach This book gives you a glimpse of Machine Learning Models and the application of models at scale using clustering, classification, regression and reinforcement learning with fun examples. Hands-on examples will be presented to understand the power of problem solving with Machine Learning and Advanced architectures, software installation, and configuration.
Mathematics For Machine Learning
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Author : Marc Peter Deisenroth
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-23
Mathematics For Machine Learning written by Marc Peter Deisenroth 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 2020-04-23 with Computers categories.
Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.
Machine Learning Fundamentals
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Author : Hui Jiang
language : en
Publisher: Cambridge University Press
Release Date : 2021-11-25
Machine Learning Fundamentals written by Hui Jiang 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 2021-11-25 with Computers categories.
A coherent introduction to core concepts and deep learning techniques that are critical to academic research and real-world applications.
Machine Learning For Science And Engineering Volume 1 Fundamentals
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Author : Herman Jaramillo
language : en
Publisher: SEG Books
Release Date : 2023-04-01
Machine Learning For Science And Engineering Volume 1 Fundamentals written by Herman Jaramillo and has been published by SEG Books this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-01 with Science categories.
This textbook teaches underlying mathematics, terminology, and programmatic skills to implement, test, and apply machine learning to real-world problems. Exercises with field data, including well logs and weather measurements, prepare and encourage readers to begin using software to validate results and program their own creative data solutions. As the size and complexity of data soars exponentially, machine learning (ML) has gained prominence in applications in geoscience and related fields. ML-powered technology increasingly rivals or surpasses human performance and fuels a large range of leading-edge research. This textbook teaches the underlying mathematics, terminology, and programmatic skills to implement, test, and apply ML to real-world problems. It builds the mathematical pillars required to thoroughly comprehend and master modern ML concepts and translates the newly gained mathematical understanding into better applied data science. Exercises with raw field data, including well logs and weather measurements, prepare and encourage the reader to begin using software to validate results and program their own creative data solutions. Most importantly, the reader always keeps an eye on the ML’s imperfect data situations as encountered in the real world.
Introduction To Machine Learning And Natural Language Processing
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Author : Dr.Ravi Kumar Saidala
language : en
Publisher: SK Research Group of Companies
Release Date : 2024-07-19
Introduction To Machine Learning And Natural Language Processing written by Dr.Ravi Kumar Saidala and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-19 with Computers categories.
Dr.Ravi Kumar Saidala, Associate Professor, Department of CSE – Data Science, CMR University, Bangalore, Karnataka, India. Mr.Satyanarayanareddy Marri, Assistant Professor, Department of Artificial Intelligence, Anurag University, Hyderabad, Telangana, India. Dr.D.Usha Rani, Associate Professor, Department of Computer Science and Applications, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India. Prof.U.Ananthanagu, Assistant Professor, Department of CSE, Alliance University, Bangalore, Karnataka, India.
Machine Learning Mastery Deep Learning And Natural Language Processing Integration
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Author : Dr.Talluri.Sunil Kumar
language : en
Publisher: SK Research Group of Companies
Release Date : 2024-07-24
Machine Learning Mastery Deep Learning And Natural Language Processing Integration written by Dr.Talluri.Sunil Kumar and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-24 with Computers categories.
Dr.Talluri.Sunil Kumar, Professor, Department of CSE-(CyS, DS) and AI&DS, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India. Dr.Sagar Yeruva, Associate Professor, Department of CSE - AIML & IoT, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India.
Machine Learning Deep Learning In Natural Language Processing
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Author : Dr.S. Ramesh
language : en
Publisher: Leilani Katie Publication
Release Date : 2024-02-05
Machine Learning Deep Learning In Natural Language Processing written by Dr.S. Ramesh and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-05 with Computers categories.
Dr.S. Ramesh, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.J.Chenni Kumaran, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.M.Sivaram, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.A.Manimaran, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.A.Selvakumar, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.