Deep Learning Fundamentals Theory And Applications
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Deep Learning Fundamentals Theory And Applications
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Author : Kaizhu Huang
language : en
Publisher: Springer
Release Date : 2019-02-15
Deep Learning Fundamentals Theory And Applications written by Kaizhu Huang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-15 with Medical categories.
The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.
Deep Learning
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Author : Kaizhu Huang
language : en
Publisher:
Release Date : 2019
Deep Learning written by Kaizhu Huang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with COMPUTERS categories.
The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.
Deep Learning Fundamentals Theory And Applications
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Author : Dr. R. Kanagaraj
language : en
Publisher: AG PUBLISHING HOUSE (AGPH Books)
Release Date :
Deep Learning Fundamentals Theory And Applications written by Dr. R. Kanagaraj and has been published by AG PUBLISHING HOUSE (AGPH Books) this book supported file pdf, txt, epub, kindle and other format this book has been release on with Study Aids categories.
More complex computing approaches have grown in popularity as technology has improved and big data has emerged. Increasing customer demand for better goods, as well as firms trying to better exploit their resources, have been driving this trend. Machine learning is a field that combines statistics, mathematics, and computer science to create and analyze algorithms that improve their own behavior in an iterative fashion by design. Initially, the discipline was committed to the development of artificial intelligence, but owing to the constraints of theory and technology at the time, it became more reasonable to concentrate these algorithms on particular tasks. Deep learning is a sort of machine learning and artificial intelligence (AI) that mimics how people acquire certain types of knowledge. Deep learning is a critical component of data science, which also covers statistics and predictive modeling. Deep learning is particularly advantageous to data scientists who are responsible with gathering, analyzing, and interpreting massive volumes of data; deep learning speeds up and simplifies this process. In this book the concept of deep learning under the machine learning is explained in every aspect. Whether, it's their fundamental concepts or the application of deep learning on daily basis.
Machine Learning Fundamentals Theory Algorithms And Real World Applications
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Author : Dr.V.Kumaresan
language : en
Publisher: Leilani Katie Publication
Release Date : 2025-05-03
Machine Learning Fundamentals Theory Algorithms And Real World Applications written by Dr.V.Kumaresan 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 2025-05-03 with Computers categories.
Dr.V.Kumaresan, Assistant Professor, Department of Computer Science, Arignar Anna Government Arts College, Namakkal, Tamil Nadu, India. Dr. Mukta Makhija, Professor, Assistant Dean - IT, Head - Research and Innovation Cell, Department of Computer Applications, Integrated Academy of Management and Technology((INMANTEC), Ghaziabad, Uttar Pradesh, India. Prof.Kamal Nain, Assistant Professor, Department of Information Technology, Integrated Academy of Management and Technology (INMANTEC), Ghaziabad, Uttar Pradesh, India. Dr.V.A.Jane, Assistant Professor, Department of B.Voc SD and SA, St. Joseph's College (Autonomous), Tiruchirapalli , Tamil Nadu, India. Dr.G.Stephen, Assistant Librarian, St. Xavier's University, Kolkata, West Bengal.
Metaheuristics In Machine Learning Theory And Applications
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Author : Diego Oliva
language : en
Publisher: Springer Nature
Release Date : 2021-07-13
Metaheuristics In Machine Learning Theory And Applications written by Diego Oliva and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-13 with Computers categories.
This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.
Deep Learning
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Author : Bhavatarini N
language : en
Publisher: MileStone Research Publications
Release Date : 2022-09-09
Deep Learning written by Bhavatarini N and has been published by MileStone Research Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-09 with Computers categories.
In a very short time, deep learning has become a widely useful technique, solving and automating problems in computer vision, robotics, healthcare, physics, biology, and beyond. One of the delightful things about deep learning is its relative simplicity. Powerful deep learning software has been built to make getting started fast and easy. In a few weeks, you can understand the basics and get comfortable with the techniques. This opens up a world of creativity. You start applying it to problems that have data at hand, and you feel wonderful seeing a machine solving problems for you. However, you slowly feel yourself getting closer to a giant barrier. You built a deep learning model, but it doesn’t work as well as you had hoped. This is when you enter the next stage, finding and reading state-of-the-art research on deep learning. However, there’s a voluminous body of knowledge on deep learning, with three decades of theory, techniques, and tooling behind it. As you read through some of this research, you realize that humans can explain simple things in really complicated ways. Scientists use words and mathematical notation in these papers that appear foreign, and no textbook or blog post seems to cover the necessary background that you need in accessible ways. Engineers and programmers assume you know how GPUs work and have knowledge about obscure tools.
Mathematical Principles In Machine Learning
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Author : Syed Thouheed Ahmed
language : en
Publisher: MileStone Research Publications
Release Date : 2023-06-15
Mathematical Principles In Machine Learning written by Syed Thouheed Ahmed and has been published by MileStone Research Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-15 with Computers categories.
Machine learning, artificial intelligence (AI), and cognitive computing are dominating conversations about how emerging advanced analytics can provide businesses with a competitive advantage to the business. There is no debate that existing business leaders are facing new and unanticipated competitors. These businesses are looking at new strategies that can prepare them for the future. While a business can try different strategies, they all come back to a fundamental truth. If you’re curious about machine learning, this book is a wonderful way to immerse yourself in key concepts, terminology, and trends. We’ve curated a list of machine learning topics for beginners, from general overviews to those with focus areas, such as statistics, deep learning, and predictive analytics. With this book on your reading list, you’ll be able to: Determine whether a career in machine learning is right for you Learn what skills you’ll need as a machine learning engineer or data scientist Knowledge that can help you find and prepare for job interviews Stay on top of the latest trends in machine learning and artificial intelligence
Modern Approaches In Machine Learning And Cognitive Science A Walkthrough
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Author : Vinit Kumar Gunjan
language : en
Publisher: Springer Nature
Release Date : 2021-04-26
Modern Approaches In Machine Learning And Cognitive Science A Walkthrough written by Vinit Kumar Gunjan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-26 with Technology & Engineering categories.
This book provides a systematic and comprehensive overview of machine learning with cognitive science methods and technologies which have played an important role at the core of practical solutions for a wide scope of tasks between handheld apps, industrial process control, autonomous vehicles, environmental policies, life sciences, playing computer games, computational theory, and engineering development. The chapters in this book focus on readers interested in machine learning, cognitive and neuro-inspired computational systems – theories, mechanisms, and architecture, which underline human and animal behaviour, and their application to conscious and intelligent systems. In the current version, it focuses on the successful implementation and step-by-step explanation of practical applications of the domain. It also offers a wide range of inspiring and interesting cutting-edge contributions to applications of machine learning and cognitive science such as healthcare products, medical electronics, and gaming. Overall, this book provides valuable information on effective, cutting-edge techniques and approaches for students, researchers, practitioners, and academicians working in the field of AI, neural network, machine learning, and cognitive science. Furthermore, the purpose of this book is to address the interests of a broad spectrum of practitioners, students, and researchers, who are interested in applying machine learning and cognitive science methods in their respective domains.
Smart Sensor Networks
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Author : Umang Singh
language : en
Publisher: Springer Nature
Release Date : 2021-09-01
Smart Sensor Networks written by Umang Singh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-01 with Computers categories.
This book provides IT professionals, educators, researchers, and students a compendium of knowledge on smart sensors and devices, types of sensors, data analysis and monitoring with the help of smart sensors, decision making, impact of machine learning algorithms, and artificial intelligence-related methodologies for data analysis and understanding of smart applications in networks. Smart sensor networks play an important role in the establishment of network devices which can easily interact with physical world through plethora of variety of sensors for collecting and monitoring the surrounding context and allowing environment information. Apart from military applications, smart sensor networks are used in many civilian applications nowadays and there is a need to manage high volume of demands in related applications. This book comprises of 9 chapters and presents a valuable insight on the original research and review articles on the latest achievements that contributes to the field of smart sensor networks and their usage in real-life applications like smart city, smart home, e-healthcare, smart social sensing networks, etc. Chapters illustrate technological advances and trends, examine research opportunities, highlight best practices and standards, and discuss applications and adoption. Some chapters also provide holistic and multiple perspectives while examining the impact of smart sensor networks and the role of data analytics, data sharing, and its control along with future prospects.
Natural Language Processing Practical Approach
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Author : Syed Muzamil Basha
language : en
Publisher: MileStone Research Publications
Release Date : 2023-02-26
Natural Language Processing Practical Approach written by Syed Muzamil Basha and has been published by MileStone Research Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-26 with Computers categories.
The "Natural Language Processing Practical Approach" is a textbook that provides a practical introduction to the field of Natural Language Processing (NLP). The goal of the textbook is to provide a hands-on, practical guide to NLP, with a focus on real-world applications and use cases. The textbook covers a range of NLP topics, including text preprocessing, sentiment analysis, named entity recognition, text classification, and more. The textbook emphasizes the use of algorithms and models to solve NLP problems and provides practical examples and code snippets in various programming languages, including Python. The textbook is designed for students, researchers, and practitioners in NLP who want to gain a deeper understanding of the field and build their own NLP projects. The current state of NLP is rapidly evolving with advancements in machine learning and deep learning techniques. The field has seen a significant increase in research and development efforts in recent years, leading to improved performance and new applications in areas such as sentiment analysis, text classification, language translation, and named entity recognition. The future prospects of NLP are bright, with continued development in areas such as reinforcement learning, transfer learning, and unsupervised learning, which are expected to further improve the performance of NLP models. Additionally, increasing amounts of text data available through the internet and growing demand for human-like conversational interfaces in areas such as customer service and virtual assistants will likely drive further advancements in NLP. The benefits of a hands-on, practical approach to natural language processing include: 1. Improved understanding: Practical approaches allow students to experience the concepts and techniques in action, helping them to better understand how NLP works. 2. Increased motivation: Hands-on approaches to learning can increase student engagement and motivation, making the learning process more enjoyable and effective. 3. Hands-on experience: By working with real data and implementing NLP techniques, students gain hands-on experience in applying NLP techniques to real-world problems. 4. Improved problem-solving skills: Practical approaches help students to develop problem-solving skills by working through real-world problems and challenges. 5. Better retention: When students have hands-on experience with NLP techniques, they are more likely to retain the information and be able to apply it in the future. A comprehensive understanding of NLP would include knowledge of its various tasks, techniques, algorithms, challenges, and applications. It also involves understanding the basics of computational linguistics, natural language understanding, and text representation methods such as tokenization, stemming, and lemmatization. Moreover, hands-on experience with NLP tools and libraries like NLTK, Spacy, and PyTorch would also enhance one's understanding of NLP.