Deep Learning Models Foundations And Applications

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Deep Learning Models Foundations And Applications

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Author : Mr.Balingan Sangameshwar
language : en
Publisher: Leilani Katie Publication
Release Date : 2025-05-01

Deep Learning Models Foundations And Applications written by Mr.Balingan Sangameshwar 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 2025-05-01 with Computers categories.

Mr.Balingan Sangameshwar, Assistant Professor, Department of Computer Science and Engineering - (CyS, DS) and AI & DS, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology, Hyderabad, Telangana, India. Mr.Vonteru Srikanth Reddy, Assistant Professor, Department of Computer Science and Engineering (Data Science), Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology, Hyderabad, Telangana, India. Mr.P.Praveen, Assistant Professor, Department of Computer Science and Engineering, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology, Hyderabad, Telangana, India. Mr.Sudheer Nidamanuri, Assistant Professor, Department of Computer Science and Engineering - (CyS, DS) and AI & DS, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology, Hyderabad, Telangana, India

Machine Learning Foundations And Applications A Practical Guide To Supervised Unsupervised And Reinforcement Learning

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Author : Jarrel E.
language : en
Publisher: Jarrel E.
Release Date : 2025-05-09

Machine Learning Foundations And Applications A Practical Guide To Supervised Unsupervised And Reinforcement Learning written by Jarrel E. and has been published by Jarrel E. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-09 with Computers categories.

Master the algorithms powering today’s AI revolution. This practical guide breaks down the foundations of machine learning into clear, structured lessons—covering supervised learning, unsupervised learning, and reinforcement learning. Whether you're a student, developer, or data professional, you'll learn how real-world models like linear regression, neural networks, support vector machines, PCA, and Q-learning actually work—mathematically and computationally. This book blends theory with implementation, offering step-by-step explanations, intuitive insights, and practical tools for applying machine learning in business, research, and product development. If you're serious about learning machine learning, this is the book that takes you from first principles to advanced concepts—with clarity, depth, and purpose.

Data Science Foundations And Applications

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Author : Xintao Wu
language : en
Publisher: Springer Nature
Release Date : 2025-07-21

Data Science Foundations And Applications written by Xintao Wu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-21 with Computers categories.

The two-volume set LNAI 15875 + 15876 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025 Special Session, held in Sydney, NSW, Australia, during June 10–13, 2025. The 68 full papers included in this set were carefully reviewed and selected from 696 submissions. They were organized in topical sections as follows: survey track; machine learning; trustworthiness; learning on complex data; graph mining; machine learning applications; representation learning; scientific/business data analysis; and special track on large language models.

Deep Learning Models And Its Application An Overview With The Help Of R Software Second In Series Machine Learning

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Author : Editor IJSMI
language : en
Publisher: International Journal of Statistics and Medical Informatics
Release Date : 2019-02-09

Deep Learning Models And Its Application An Overview With The Help Of R Software Second In Series Machine Learning written by Editor IJSMI and has been published by International Journal of Statistics and Medical Informatics this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-09 with Computers categories.

Deep Learning Models and its application: An overview with the help of R softwarePrefaceDeep learning models are widely used in different fields due to its capability to handle large and complex datasets and produce the desired results with more accuracy at a greater speed. In Deep learning models, features are selected automatically through the iterative process wherein the model learns the features by going deep into the dataset and selects the features to be modeled. In the traditional models the features of the dataset needs to be specified in advance. The Deep Learning algorithms are derived from Artificial Neural Network concepts and it is a part of broader Machine Learning Models. This book intends to provide an overview of Deep Learning models, its application in the areas of image recognition & classification, sentiment analysis, natural language processing, stock market prediction using R statistical software package, an open source software package. The book also includes an introduction to python software package which is also open source software for the benefit of the users.This books is a second book in series after the author’s first book- Machine Learning: An Overview with the Help of R Software https://www.amazon.com/dp/B07KQSN447EditorInternational Journal of Statistics and Medical Informaticswww.ijsmi.com/book.php

Recent Developments And The New Directions Of Research Foundations And Applications

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Author : Shahnaz N. Shahbazova
language : en
Publisher: Springer Nature
Release Date : 2023-06-26

Recent Developments And The New Directions Of Research Foundations And Applications written by Shahnaz N. Shahbazova and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-26 with Technology & Engineering categories.

This book is a collection of papers presented during the 8th World Conference on Soft Computing in February 2022. The papers cover multiple areas important for soft computing. Some papers are dedicated to fundamental aspects of soft computing, i.e., fuzzy mathematics, type-2 fuzzy sets, evolutionary-based optimization, aggregation, and neural networks. Others emphasize the application of soft computing methods to data analysis, image processing, decision-making, classification, series prediction, economics, control, and modeling.

Deep Learning

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Author : Manish Soni
language : en
Publisher:
Release Date : 2024-11-13

Deep Learning written by Manish Soni and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-13 with Computers categories.

Welcome to "Deep Learning: A Comprehensive Guide," a book meticulously designed to cater to the needs of learners at various stages of their journey into the fascinating world of deep learning. Whether you are a beginner embarking on your first exploration into artificial intelligence or a seasoned professional looking to deepen your expertise, this book aims to be your trusted companion. Deep learning, a subset of machine learning, has revolutionized the field of artificial intelligence, enabling advancements that were once thought to be the stuff of science fiction. From autonomous vehicles to sophisticated natural language processing systems, deep learning has become the backbone of many cutting-edge technologies. Understanding and mastering deep learning is not just a desirable skill but a necessity for anyone looking to thrive in the modern tech landscape. What This Book Offers This book is not just a theoretical exposition but a practical guide designed to provide you with a holistic learning experience. Here's a glimpse of what you can expect: Structured Content: Starts with neural network basics and advances to topics like convolutional, recurrent, and generative adversarial networks. Each chapter builds on the previous, ensuring a comprehensive learning journey. Online Practice Questions: Each chapter includes practice questions from basic to advanced levels to test and reinforce your understanding. Videos: Instructional videos complement the book's content, offering step-by-step explanations and real-life applications. Exercises and Projects: Includes exercises and hands-on projects that simulate real-world problems, providing practical experience. Lab Activities: Features lab activities using frameworks like TensorFlow and PyTorch for hands-on experimentation with deep learning models. Case Studies: Illustrates the application of deep learning in industries such as healthcare, finance, and entertainment, highlighting its transformative potential. Comprehensive Coverage: Covers a broad spectrum of topics, from theoretical foundations to practical implementations, latest advancements, ethical considerations, and future trends. Who Should Use This Book? This book is designed for: Students and Academics: Pursuing studies in computer science, data science, or related fields. Industry Professionals: Enhancing skills or transitioning into roles involving deep learning. Embarking on the journey to master deep learning is both challenging and rewarding. This book is designed to make that journey as smooth and enlightening as possible. We hope that the combination of theoretical knowledge, practical exercises, projects, and real-world applications will equip you with the skills and confidence needed to excel in the field of deep learning.

Deep Learning

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Author :
language : en
Publisher: Elsevier
Release Date : 2023-02-28

Deep Learning written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-28 with Mathematics categories.

Deep Learning, Volume 48 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Generative Adversarial Networks for Biometric Synthesis, Data Science and Pattern Recognition, Facial Data Analysis, Deep Learning in Electronics, Pattern Recognition, Computer Vision and Image Processing, Mechanical Systems, Crop Technology and Weather, Manipulating Faces for Identity Theft via Morphing and Deepfake, Biomedical Engineering, and more. - Provides the authority and expertise of leading contributors from an international board of authors - Presents the latest release in the Handbook of Statistics series - Includes the latest information on Deep Learning

Foundations Of Deep Learning

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Author : Fengxiang He
language : en
Publisher: Springer Nature
Release Date : 2025-02-01

Foundations Of Deep Learning written by Fengxiang He and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-01 with Computers categories.

Deep learning has significantly reshaped a variety of technologies, such as image processing, natural language processing, and audio processing. The excellent generalizability of deep learning is like a “cloud” to conventional complexity-based learning theory: the over-parameterization of deep learning makes almost all existing tools vacuous. This irreconciliation considerably undermines the confidence of deploying deep learning to security-critical areas, including autonomous vehicles and medical diagnosis, where small algorithmic mistakes can lead to fatal disasters. This book seeks to explaining the excellent generalizability, including generalization analysis via the size-independent complexity measures, the role of optimization in understanding the generalizability, and the relationship between generalizability and ethical/security issues. The efforts to understand the excellent generalizability are following two major paths: (1) developing size-independent complexity measures, which can evaluate the “effective” hypothesis complexity that can be learned, instead of the whole hypothesis space; and (2) modelling the learned hypothesis through stochastic gradient methods, the dominant optimizers in deep learning, via stochastic differential functions and the geometry of the associated loss functions. Related works discover that over-parameterization surprisingly bring many good properties to the loss functions. Rising concerns of deep learning are seen on the ethical and security issues, including privacy preservation and adversarial robustness. Related works also reveal an interplay between them and generalizability: a good generalizability usually means a good privacy-preserving ability; and more robust algorithms might have a worse generalizability. We expect readers can have a big picture of the current knowledge in deep learning theory, understand how the deep learning theory can guide new algorithm designing, and identify future research directions. Readers need knowledge of calculus, linear algebra, probability, statistics, and statistical learning theory.

Foundations Of Data Science Principles And Applications

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Author : Dr. Punit Kumar Chaubey
language : en
Publisher: Academic Guru Publishing House
Release Date : 2024-07-24

Foundations Of Data Science Principles And Applications written by Dr. Punit Kumar Chaubey 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 2024-07-24 with Study Aids categories.

The thorough reference "Foundations of Data Science: Principles and Applications" explores the fundamental ideas and real-world uses of data science. The book is appropriate for both novices and those wishing to expand their knowledge of the subject since it is organised to lead readers from the fundamentals to more complex subjects. The book is broken up into many important parts. The first few chapters cover foundational ideas such data types, statistical procedures, and data visualisation strategies. Big data analytics, data mining methods, and machine learning algorithms are introduced throughout the book. A combination of practical examples and theoretical insights are used to convey each subject, making sure that readers not only understand the ideas but also learn how to apply them. The book encourages readers to think about the wider effect of their work by addressing the ethical and social aspects of data science in addition to its technical content. Readers will have a solid foundation in data science at the conclusion of the book, along with the know-how to take on challenging data problems and make significant contributions to the field.

Deep Learning And Its Applications

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Author : Dr. S. Manikandan
language : en
Publisher: Quing Publications
Release Date : 2022-12-30

Deep Learning And Its Applications written by Dr. S. Manikandan and has been published by Quing Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-30 with Computers categories.

Deep Learning and its Applications book chapter is intended to provide various deep insight about Deep learning in various applications. According to current Industry 4.0 standards, Deep learning on the emerging research area to give various services to IT and ITeS. In this book chapter various real time applications are taken for evaluating deep learning approach. Deep Learning is the subset of machine learning which has further learned results of artificial intelligent applications. Artificial Intelligent is the current scenario for making effective decisions. Here the applications are medical image processing, moving objects, image analysis, classification, clustering, prediction, and restoration used to identify various results. Based on each chapter different problems are taken for evaluation and apply different deep learning principles to find accuracy, precision, and score functions. Supervised and Unsupervised learning techniques, TensorFlow, Yolo classifier and Colabs are used to simulate the applications. In this book chapters are very useful for researchers, students, and faculty community to learn about Deep Learning in current trends.