Mathematics For Machine Learning
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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.
Algorithmic Mathematics In Machine Learning
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Author : Bastian Bohn
language : en
Publisher: SIAM
Release Date : 2024-04-08
Algorithmic Mathematics In Machine Learning written by Bastian Bohn and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-08 with Computers categories.
This unique book explores several well-known machine learning and data analysis algorithms from a mathematical and programming perspective. The authors present machine learning methods, review the underlying mathematics, and provide programming exercises to deepen the reader’s understanding; accompany application areas with exercises that explore the unique characteristics of real-world data sets (e.g., image data for pedestrian detection, biological cell data); and provide new terminology and background information on mathematical concepts, as well as exercises, in “info-boxes” throughout the text. Algorithmic Mathematics in Machine Learning is intended for mathematicians, computer scientists, and practitioners who have a basic mathematical background in analysis and linear algebra but little or no knowledge of machine learning and related algorithms. Researchers in the natural sciences and engineers interested in acquiring the mathematics needed to apply the most popular machine learning algorithms will also find this book useful. This book is appropriate for a practical lab or basic lecture course on machine learning within a mathematics curriculum.
Mathematics For Machine Learning
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Author : Nibedita Sahu
language : en
Publisher: Nibedita Sahu
Release Date : 2023-08-25
Mathematics For Machine Learning written by Nibedita Sahu and has been published by Nibedita Sahu this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-25 with categories.
"Mathematics for Machine Learning: A Deep Dive into Algorithms" is a comprehensive guide that bridges the gap between mathematical theory and practical applications in the dynamic world of machine learning. Whether you're a data science enthusiast, a budding machine learning engineer, or a seasoned practitioner, this book equips you with the essential mathematical foundations that power cutting-edge algorithms and data-driven insights. Starting with the fundamentals of linear algebra, multivariable calculus, probability, and statistics, Nibedita expertly guides you through the intricate maze of mathematical concepts. From there, you'll explore the depths of linear regression, classification, support vector machines, neural networks, and more, all while unraveling the underlying mathematical principles that make these algorithms tick. This book isn't just about equations and formulas--it's about unlocking the potential of machine learning through a strong mathematical intuition. Nibedita's clear explanations, illustrative examples, and practical insights ensure that you not only grasp the core concepts but also discover how they translate into real-world solutions. Dive into the intricacies of convolutional and recurrent neural networks, grasp the significance of regularization techniques, and explore the ethical dimensions of AI and machine learning. Whether you're seeking to build a solid foundation for a career in data science or aiming to deepen your understanding of machine learning algorithms, "Mathematics for Machine Learning" empowers you to harness the power of mathematics as a tool for innovation and transformation in the digital age.
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.
Hands On Mathematics For Deep Learning
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Author : Jay Dawani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-06-12
Hands On Mathematics For Deep Learning written by Jay Dawani 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 2020-06-12 with Computers categories.
A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.
Math For Machine Learning
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Author : Richard Han
language : en
Publisher:
Release Date : 2018
Math For Machine Learning written by Richard Han and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
"Would you like to learn a mathematics subject that is crucial for many high-demand lucrative career fields such as: Computer Science, Data Science. Artificial Intelligence. If you're looking to gain a solid foundation in Machine Learning to further your career goals, in a way that allows you to study on your own schedule at a fraction of the cost it would take at a traditional university, this online course is for you. If you're a working professional needing a refresher on machine learning or a complete beginner who needs to learn Machine Learning for the first time, this online course is for you. Why you should take this online course: You need to refresh your knowledge of machine learning for your career to earn a higher salary. You need to learn machine learning because it is a required mathematical subject for your chosen career field such as data science or artificial intelligence. You intend to pursue a masters degree or PhD, and machine learning is a required or recommended subject. Why you should choose this instructor: I earned my PhD in Mathematics from the University of California, Riverside. I have created many successful online math courses that students around the world have found invaluable--courses in linear algebra, discrete math, and calculus."--Resource description page.
The Mathematics Of Machine Learning
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Author : Maria Han Veiga
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-05-20
The Mathematics Of Machine Learning written by Maria Han Veiga and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-20 with Mathematics categories.
This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known supervised machine learning algorithms, detailing the existing theory to provide some theoretical guarantees, featuring intuitive proofs and exposition of the material in a concise and precise manner. A broad set of topics is covered, giving an overview of the field. A summary of the topics covered is: statistical learning theory, approximation theory, linear models, kernel methods, Gaussian processes, deep neural networks, ensemble methods and unsupervised learning techniques, such as clustering and dimensionality reduction. This book is suited for students who are interested in entering the field, by preparing them to master the standard tools in Machine Learning. The reader will be equipped to understand the main theoretical questions of the current research and to engage with the field.
Mathematics For Machine Learning
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Author : Marc Peter Deisenroth
language : en
Publisher:
Release Date : 2019-12
Mathematics For Machine Learning written by Marc Peter Deisenroth and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12 with Machine learning categories.
Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.
Math Application
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Author : Vincenza Nowell
language : en
Publisher:
Release Date : 2021-03-25
Math Application written by Vincenza Nowell and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-25 with categories.
Are you interested in learning about the amazing capabilities of machine learning, but you're worried it will be just too complicated? Machine learning is an incredible technology which we're only just beginning to understand. This guide breaks down the fundamentals of machine learning in a way that anyone can understand. About the different kinds of machine learning models, neural networks, and the way these models learn data, you'll find everything you need to know to get started with machine learning in a concise, easy-to-understand way.
Math For Machine Learning
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Author : Richard Han
language : en
Publisher:
Release Date : 2018-07-12
Math For Machine Learning written by Richard Han and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-12 with Mathematics categories.
This book explains the math behind machine learning using simple but concrete examples. This book will get you started in machine learning in a smooth and natural way, preparing you for more advanced topics and dispelling the belief that machine learning is complicated, difficult, and intimidating.