Machine Learning Theoretical Foundations And Practical Applications
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Machine Learning Theoretical Foundations And Practical Applications
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Author : Manjusha Pandey
language : en
Publisher: Springer Nature
Release Date : 2021-04-19
Machine Learning Theoretical Foundations And Practical Applications written by Manjusha Pandey and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-19 with Technology & Engineering categories.
This edited book is a collection of chapters invited and presented by experts at 10th industry symposium held during 9–12 January 2020 in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network learning, knowledge acquisition and learning, knowledge intensive learning, machine learning and information retrieval, machine learning for web navigation and mining, learning through mobile data mining, text and multimedia mining through machine learning, distributed and parallel learning algorithms and applications, feature extraction and classification, theories and models for plausible reasoning, computational learning theory, cognitive modelling and hybrid learning algorithms.
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard 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 2020-06-29 with Computers categories.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Understanding Machine Learning
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Author : Shai Shalev-Shwartz
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-19
Understanding Machine Learning written by Shai Shalev-Shwartz 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 2014-05-19 with Computers categories.
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Deep Learning
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Author : Ian Goodfellow
language : en
Publisher: MIT Press
Release Date : 2016-11-10
Deep Learning written by Ian Goodfellow and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-10 with Computers categories.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Exploring Machine Learning Theory Practice And Innovations
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Author : Dr. Vanitha Kakollu
language : en
Publisher: Academic Guru Publishing House
Release Date : 2024-12-23
Exploring Machine Learning Theory Practice And Innovations written by Dr. Vanitha Kakollu and has been published by Academic Guru Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-23 with Study Aids categories.
“Exploring Machine Learning: Theory, Practice, and Innovations” is a thoughtfully curated resource that bridges the gap between foundational concepts and advanced methodologies in machine learning. With its systematic structure and practical orientation, the book caters to both beginners and experienced professionals in the field. The content is meticulously organised to align with the learner’s journey in understanding machine learning. The first chapter lays the groundwork by distinguishing human learning from machine learning, elucidating key concepts, and highlighting the potential and limitations of machine learning applications. A dedicated section on data preparation ensures readers grasp the significance of data preprocessing, quality enhancement, and exploration, setting the stage for successful modeling. The book’s core chapters address model selection, training, evaluation, and optimisation while introducing pivotal feature engineering techniques. Readers are guided through Bayes’ Theorem and its role in concept learning, followed by an exploration of supervised and unsupervised learning methods. Advanced algorithms, including decision trees, neural networks, and clustering techniques, are explained with clarity and context. Deep learning and neural networks are given special attention, with a focus on architecture, activation functions, and learning processes. The inclusion of contemporary topics such as ensemble learning and regularisation highlights the text’s relevance in modern machine learning landscapes. Practical insights are enriched by case studies across diverse applications, showcasing how theory translates into innovation. “Exploring Machine Learning” serves as a comprehensive, accessible, and indispensable guide for navigating the dynamic world of machine learning.
The Principles Of Deep Learning Theory
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Author : Daniel A. Roberts
language : en
Publisher: Cambridge University Press
Release Date : 2022-05-26
The Principles Of Deep Learning Theory written by Daniel A. Roberts 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 2022-05-26 with Computers categories.
This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Machine Learning
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Author : RODRIGO F MELLO
language : en
Publisher: Springer
Release Date : 2018-08-01
Machine Learning written by RODRIGO F MELLO and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-01 with Computers categories.
This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible. It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory. Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.
Foundations Of Machine Learning Second Edition
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Author : Mehryar Mohri
language : en
Publisher: MIT Press
Release Date : 2018-12-25
Foundations Of Machine Learning Second Edition written by Mehryar Mohri and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-25 with Computers categories.
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
Methodologies Frameworks And Applications Of Machine Learning
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Author : Srivastava, Pramod Kumar
language : en
Publisher: IGI Global
Release Date : 2024-03-22
Methodologies Frameworks And Applications Of Machine Learning written by Srivastava, Pramod Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-22 with Computers categories.
Technology is constantly evolving, and machine learning is positioned to become a pivotal tool with the power to transform industries and revolutionize everyday life. This book underscores the urgency of leveraging the latest machine learning methodologies and theoretical advancements, all while harnessing a wealth of realistic data and affordable computational resources. Machine learning is no longer confined to theoretical domains; it is now a vital component in healthcare, manufacturing, education, finance, law enforcement, and marketing, ushering in an era of data-driven decision-making. Academic scholars seeking to unlock the potential of machine learning in the context of Industry 5.0 and advanced IoT applications will find that the groundbreaking book, Methodologies, Frameworks, and Applications of Machine Learning, introduces an unmissable opportunity to delve into the forefront of modern research and application. This book offers a wealth of knowledge and practical insights across a wide array of topics, ranging from conceptual frameworks and methodological approaches to the application of probability theory, statistical techniques, and machine learning in domains as diverse as e-government, healthcare, cyber-physical systems, and sustainable development, this comprehensive guide equips you with the tools to navigate the complexities of Industry 5.0 and the Internet of Things (IoT).
Artificial Intelligence And Machine Learning In Management Science Emerging Research And Applications
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Author : Ms. Meenu Shukla
language : en
Publisher: NC Publishers
Release Date : 2025-07-01
Artificial Intelligence And Machine Learning In Management Science Emerging Research And Applications written by Ms. Meenu Shukla and has been published by NC Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-01 with Antiques & Collectibles categories.
As the global business environment continues to evolve, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for enhancing decision-making, optimizing operations, and fostering innovation across various sectors. This book brings together a collection of scholarly contributions from researchers and practitioners who are at the forefront of integrating these technologies with managerial practices. The chapters offer both theoretical insights and practical applications, covering domains such as operations research, strategic planning, supply chain optimization, marketing analytics, financial forecasting, and human resource management.