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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning

Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems (Paperback, 1st ed. 2021): E... Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems (Paperback, 1st ed. 2021)
E Priya, V. Rajinikanth
R3,367 Discovery Miles 33 670 Ships in 10 - 15 working days

This book comprehensively reviews the various automated and semi-automated signal and image processing techniques, as well as deep-learning-based image analysis techniques, used in healthcare diagnostics. It highlights a range of data pre-processing methods used in signal processing for effective data mining in remote healthcare, and discusses pre-processing using filter techniques, noise removal, and contrast-enhanced methods for improving image quality. The book discusses the status quo of artificial intelligence in medical applications, as well as its future. Further, it offers a glimpse of feature extraction methods for reducing dimensionality and extracting discriminatory information hidden in biomedical signals. Given its scope, the book is intended for academics, researchers and practitioners interested in the latest real-world technological innovations.

Domain Adaptation in Computer Vision with Deep Learning (Paperback, 1st ed. 2020): Hemanth Venkateswara, Sethuraman Panchanathan Domain Adaptation in Computer Vision with Deep Learning (Paperback, 1st ed. 2020)
Hemanth Venkateswara, Sethuraman Panchanathan
R4,340 Discovery Miles 43 400 Ships in 10 - 15 working days

This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation. Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation. This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.

Advances in Mechatronics, Manufacturing, and Mechanical Engineering - Selected articles from MUCET 2019 (Paperback, 1st ed.... Advances in Mechatronics, Manufacturing, and Mechanical Engineering - Selected articles from MUCET 2019 (Paperback, 1st ed. 2021)
Muhammad Aizzat Zakaria, Anwar P.P. Abdul Majeed, Mohd Hasnun Arif Hassan
R3,618 Discovery Miles 36 180 Ships in 10 - 15 working days

This book highlights selected papers from the Mechanical Engineering track, with a focus on mechatronics and manufacturing, presented at the "Malaysian Technical Universities Conference on Engineering and Technology" (MUCET 2019). The conference brings together researchers and professionals in the fields of engineering, research and technology, providing a platform for future collaborations and the exchange of ideas.

Advances in Multidisciplinary Analysis and Optimization - Proceedings of the 2nd National Conference on Multidisciplinary... Advances in Multidisciplinary Analysis and Optimization - Proceedings of the 2nd National Conference on Multidisciplinary Analysis and Optimization (Paperback, 1st ed. 2020)
Raviprakash R Salagame, Palaniappan Ramu, Indira Narayanaswamy, Dhish Kumar Saxena
R4,362 Discovery Miles 43 620 Ships in 10 - 15 working days

This volume contains select papers presented during the 2nd National Conference on Multidisciplinary Analysis and Optimization. It discusses new developments at the core of optimization methods and its application in multiple applications. The papers showcase fundamental problems and applications which include domains such as aerospace, automotive and industrial sectors. The variety of topics and diversity of insights presented in the general field of optimization and its use in design for different applications will be of interest to researchers in academia or industry.

Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation - Theory and Practice of Hazard Mitigation... Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation - Theory and Practice of Hazard Mitigation (Paperback, 1st ed. 2021)
Ravinesh C. Deo, Pijush Samui, Ozgur Kisi, Zaher Mundher Yaseen
R5,143 Discovery Miles 51 430 Ships in 10 - 15 working days

This book highlights cutting-edge applications of machine learning techniques for disaster management by monitoring, analyzing, and forecasting hydro-meteorological variables. Predictive modelling is a consolidated discipline used to forewarn the possibility of natural hazards. In this book, experts from numerical weather forecast, meteorology, hydrology, engineering, agriculture, economics, and disaster policy-making contribute towards an interdisciplinary framework to construct potent models for hazard risk mitigation. The book will help advance the state of knowledge of artificial intelligence in decision systems to aid disaster management and policy-making. This book can be a useful reference for graduate student, academics, practicing scientists and professionals of disaster management, artificial intelligence, and environmental sciences.

Cellular Learning Automata: Theory and Applications (Paperback, 1st ed. 2021): Reza Vafashoar, Hossein Morshedlou, Alireza... Cellular Learning Automata: Theory and Applications (Paperback, 1st ed. 2021)
Reza Vafashoar, Hossein Morshedlou, Alireza Rezvanian, Mohammad Reza Meybodi
R4,374 Discovery Miles 43 740 Ships in 10 - 15 working days

This book highlights both theoretical and applied advances in cellular learning automata (CLA), a type of hybrid computational model that has been successfully employed in various areas to solve complex problems and to model, learn, or simulate complicated patterns of behavior. Owing to CLA's parallel and learning abilities, it has proven to be quite effective in uncertain, time-varying, decentralized, and distributed environments. The book begins with a brief introduction to various CLA models, before focusing on recently developed CLA variants. In turn, the research areas related to CLA are addressed as bibliometric network analysis perspectives. The next part of the book presents CLA-based solutions to several computer science problems in e.g. static optimization, dynamic optimization, wireless networks, mesh networks, and cloud computing. Given its scope, the book is well suited for all researchers in the fields of artificial intelligence and reinforcement learning.

Machine Learning Paradigms - Advances in Deep Learning-based Technological Applications (Paperback, 1st ed. 2020): George A.... Machine Learning Paradigms - Advances in Deep Learning-based Technological Applications (Paperback, 1st ed. 2020)
George A. Tsihrintzis, Lakhmi C. Jain
R4,392 Discovery Miles 43 920 Ships in 10 - 15 working days

At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.

AI and Machine Learning Paradigms for Health Monitoring System - Intelligent Data Analytics (Paperback, 1st ed. 2021): Hasmat... AI and Machine Learning Paradigms for Health Monitoring System - Intelligent Data Analytics (Paperback, 1st ed. 2021)
Hasmat Malik, Nuzhat Fatema, Jafar A. Alzubi
R5,157 Discovery Miles 51 570 Ships in 10 - 15 working days

This book embodies principles and applications of advanced soft computing approaches in engineering, healthcare and allied domains directed toward the researchers aspiring to learn and apply intelligent data analytics techniques. The first part covers AI, machine learning and data analytics tools and techniques and their applications to the class of several hospital and health real-life problems. In the later part, the applications of AI, ML and data analytics shall be covered over the wide variety of applications in hospital, health, engineering and/or applied sciences such as the clinical services, medical image analysis, management support, quality analysis, bioinformatics, device analysis and operations. The book presents knowledge of experts in the form of chapters with the objective to introduce the theme of intelligent data analytics and discusses associated theoretical applications. At last, it presents simulation codes for the problems included in the book for better understanding for beginners.

Machine Intelligence - Perspectives on the Computational Model (Hardcover): Andy Clark, Torib IO Machine Intelligence - Perspectives on the Computational Model (Hardcover)
Andy Clark, Torib IO
R3,590 Discovery Miles 35 900 Ships in 12 - 19 working days

Summarizes and illuminates two decades of research
Gathering important papers by both philosophers and scientists, this collection illuminates the central themes that have arisen during the last two decades of work on the conceptual foundations of artificial intelligence and cognitive science. Each volume begins with a comprehensive introduction that places the coverage in a broader perspective and links it with material in the companion volumes. The collection is of interest in many disciplines including computer science, linguistics, biology, information science, psychology, neuroscience, iconography, and philosophy.
Examines initial efforts and the latest controversies
The topics covered range from the bedrock assumptions of the computational approach to understanding the mind, to the more recent debates concerning cognitive architectures, all the way to the latest developments in robotics, artificial life, and dynamical systems theory. The collection first examines the lineageof major research programs, beginning with the basic idea of machine intelligence itself, then focuses on specific aspects of thought and intelligence, highlighting the much-discussed issue of consciousness, the equally important, but less densely researched issue of emotional response, and the more traditionally philosophical topic of language and meaning.
Provides a gamut of perspectives
The editors have included several articles that challenge crucial elements of the familiar research program of cognitive science, as well as important writings whose previous circulation has been limited. Within each volume the papers are organized to reflect a variety of research programs and issues. Thesubstantive introductions that accompany each volume further organize the material and provide readers with a working sense of the issues and the connection between articles.

Generating a New Reality - From Autoencoders and Adversarial Networks to Deepfakes (Paperback, 1st ed.): Micheal Lanham Generating a New Reality - From Autoencoders and Adversarial Networks to Deepfakes (Paperback, 1st ed.)
Micheal Lanham
R1,657 R1,355 Discovery Miles 13 550 Save R302 (18%) Ships in 10 - 15 working days

The emergence of artificial intelligence (AI) has brought us to the precipice of a new age where we struggle to understand what is real, from advanced CGI in movies to even faking the news. AI that was developed to understand our reality is now being used to create its own reality. In this book we look at the many AI techniques capable of generating new realities. We start with the basics of deep learning. Then we move on to autoencoders and generative adversarial networks (GANs). We explore variations of GAN to generate content. The book ends with an in-depth look at the most popular generator projects. By the end of this book you will understand the AI techniques used to generate different forms of content. You will be able to use these techniques for your own amusement or professional career to both impress and educate others around you and give you the ability to transform your own reality into something new. What You Will Learn Know the fundamentals of content generation from autoencoders to generative adversarial networks (GANs) Explore variations of GAN Understand the basics of other forms of content generation Use advanced projects such as Faceswap, deepfakes, DeOldify, and StyleGAN2 Who This Book Is For Machine learning developers and AI enthusiasts who want to understand AI content generation techniques

Intelligent and Cloud Computing - Proceedings of ICICC 2019, Volume 2 (Paperback, 1st ed. 2021): Debahuti Mishra, Rajkumar... Intelligent and Cloud Computing - Proceedings of ICICC 2019, Volume 2 (Paperback, 1st ed. 2021)
Debahuti Mishra, Rajkumar Buyya, Prasant Mohapatra, Srikanta Patnaik
R4,470 Discovery Miles 44 700 Ships in 10 - 15 working days

This book features a collection of high-quality research papers presented at the International Conference on Intelligent and Cloud Computing (ICICC 2019), held at Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, India, on December 20, 2019. Including contributions on system and network design that can support existing and future applications and services, it covers topics such as cloud computing system and network design, optimization for cloud computing, networking, and applications, green cloud system design, cloud storage design and networking, storage security, cloud system models, big data storage, intra-cloud computing, mobile cloud system design, real-time resource reporting and monitoring for cloud management, machine learning, data mining for cloud computing, data-driven methodology and architecture, and networking for machine learning systems.

Neural-Network Simulation of Strongly Correlated Quantum Systems (Paperback, 1st ed. 2020): Stefanie Czischek Neural-Network Simulation of Strongly Correlated Quantum Systems (Paperback, 1st ed. 2020)
Stefanie Czischek
R2,853 Discovery Miles 28 530 Ships in 10 - 15 working days

Quantum systems with many degrees of freedom are inherently difficult to describe and simulate quantitatively. The space of possible states is, in general, exponentially large in the number of degrees of freedom such as the number of particles it contains. Standard digital high-performance computing is generally too weak to capture all the necessary details, such that alternative quantum simulation devices have been proposed as a solution. Artificial neural networks, with their high non-local connectivity between the neuron degrees of freedom, may soon gain importance in simulating static and dynamical behavior of quantum systems. Particularly promising candidates are neuromorphic realizations based on analog electronic circuits which are being developed to capture, e.g., the functioning of biologically relevant networks. In turn, such neuromorphic systems may be used to measure and control real quantum many-body systems online. This thesis lays an important foundation for the realization of quantum simulations by means of neuromorphic hardware, for using quantum physics as an input to classical neural nets and, in turn, for using network results to be fed back to quantum systems. The necessary foundations on both sides, quantum physics and artificial neural networks, are described, providing a valuable reference for researchers from these different communities who need to understand the foundations of both.

Provenance in Data Science - From Data Models to Context-Aware Knowledge Graphs (Paperback, 1st ed. 2021): Leslie F Sikos,... Provenance in Data Science - From Data Models to Context-Aware Knowledge Graphs (Paperback, 1st ed. 2021)
Leslie F Sikos, Oshani W. Seneviratne, Deborah L. McGuinness
R4,299 Discovery Miles 42 990 Ships in 10 - 15 working days

RDF-based knowledge graphs require additional formalisms to be fully context-aware, which is presented in this book. This book also provides a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations across multiple application domains, in order to demonstrate how to combine graph-based data models and provenance representations. This is important to make statements authoritative, verifiable, and reproducible, such as in biomedical, pharmaceutical, and cybersecurity applications, where the data source and generator can be just as important as the data itself. Capturing provenance is critical to ensure sound experimental results and rigorously designed research studies for patient and drug safety, pathology reports, and medical evidence generation. Similarly, provenance is needed for cyberthreat intelligence dashboards and attack maps that aggregate and/or fuse heterogeneous data from disparate data sources to differentiate between unimportant online events and dangerous cyberattacks, which is demonstrated in this book. Without provenance, data reliability and trustworthiness might be limited, causing data reuse, trust, reproducibility and accountability issues. This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.

Handbook of Big Geospatial Data (Paperback, 1st ed. 2021): Martin Werner, Yao-Yi Chiang Handbook of Big Geospatial Data (Paperback, 1st ed. 2021)
Martin Werner, Yao-Yi Chiang
R6,420 Discovery Miles 64 200 Ships in 10 - 15 working days

This handbook covers a wide range of topics related to the collection, processing, analysis, and use of geospatial data in their various forms. This handbook provides an overview of how spatial computing technologies for big data can be organized and implemented to solve real-world problems. Diverse subdomains ranging from indoor mapping and navigation over trajectory computing to earth observation from space, are also present in this handbook. It combines fundamental contributions focusing on spatio-textual analysis, uncertain databases, and spatial statistics with application examples such as road network detection or colocation detection using GPUs. In summary, this handbook gives an essential introduction and overview of the rich field of spatial information science and big geospatial data. It introduces three different perspectives, which together define the field of big geospatial data: a societal, governmental, and governance perspective. It discusses questions of how the acquisition, distribution and exploitation of big geospatial data must be organized both on the scale of companies and countries. A second perspective is a theory-oriented set of contributions on arbitrary spatial data with contributions introducing into the exciting field of spatial statistics or into uncertain databases. A third perspective is taking a very practical perspective to big geospatial data, ranging from chapters that describe how big geospatial data infrastructures can be implemented and how specific applications can be implemented on top of big geospatial data. This would include for example, research in historic map data, road network extraction, damage estimation from remote sensing imagery, or the analysis of spatio-textual collections and social media. This multi-disciplinary approach makes the book unique. This handbook can be used as a reference for undergraduate students, graduate students and researchers focused on big geospatial data. Professionals can use this book, as well as practitioners facing big collections of geospatial data.

Data Science - 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021, Taiyuan,... Data Science - 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021, Taiyuan, China, September 17-20, 2021, Proceedings, Part I (Paperback, 1st ed. 2021)
Jianchao Zeng, Pinle Qin, Weipeng Jing, Xianhua Song, Zeguang Lu
R3,214 Discovery Miles 32 140 Ships in 12 - 19 working days

This two volume set (CCIS 1451 and 1452) constitutes the refereed proceedings of the 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021 held in Taiyuan, China, in September 2021.The 81 papers presented in these two volumes were carefully reviewed and selected from 256 submissions. The papers are organized in topical sections on big data management and applications; social media and recommendation systems; infrastructure for data science; basic theory and techniques for data science; machine learning for data science; multimedia data management and analysis; social media and recommendation systems; data security and privacy; applications of data science; education research, methods and materials for data science and engineering; research demo.

Digital Interaction and Machine Intelligence - Proceedings of MIDI'2020 - 8th Machine Intelligence and Digital Interaction... Digital Interaction and Machine Intelligence - Proceedings of MIDI'2020 - 8th Machine Intelligence and Digital Interaction Conference, December 9-10, 2020, Warsaw, Poland (online) (Paperback, 1st ed. 2021)
Cezary Biele, Janusz Kacprzyk, Jan W. Owsinski, Andrzej Romanowski, Marcin Sikorski
R4,594 Discovery Miles 45 940 Ships in 10 - 15 working days

This book presents the Proceedings of MIDI'2020 - 8th Machine Intelligence and Digital Interaction Conference, December 9-10, 2020, Warsaw, Poland, held online. The rapid development of artificial intelligence (AI) and its growing applications in many fields, such as intelligent voice assistants, e-commerce (chatbots) or navigation, make end users increasingly exposed to such systems. In a world where technological solutions based on artificial intelligence are created by people for people, the final success or failure of a newly created product depends on the focus on human needs. Therefore, it is important to integrate so far independent scientific areas: broadly defined artificial intelligence and human-technology interaction. This book is intended for specialists in the above fields and attempts to integrate the perspectives of engineers and social scientists. The book is a source of inspiration as well as practical and theoretical knowledge for all readers interested in new trends in the field of user-centered AI solutions.

Deep Learning for Medical Decision Support Systems (Paperback, 1st ed. 2021): Utku Kose, Omer Deperlioglu, Jafar Alzubi, Bogdan... Deep Learning for Medical Decision Support Systems (Paperback, 1st ed. 2021)
Utku Kose, Omer Deperlioglu, Jafar Alzubi, Bogdan Patrut
R2,628 Discovery Miles 26 280 Ships in 10 - 15 working days

This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today's problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation.

The Calabi-Yau Landscape - From Geometry, to Physics, to Machine Learning (Paperback, 1st ed. 2021): Yang-hui He The Calabi-Yau Landscape - From Geometry, to Physics, to Machine Learning (Paperback, 1st ed. 2021)
Yang-hui He
R1,870 Discovery Miles 18 700 Ships in 10 - 15 working days

Can artificial intelligence learn mathematics? The question is at the heart of this original monograph bringing together theoretical physics, modern geometry, and data science. The study of Calabi-Yau manifolds lies at an exciting intersection between physics and mathematics. Recently, there has been much activity in applying machine learning to solve otherwise intractable problems, to conjecture new formulae, or to understand the underlying structure of mathematics. In this book, insights from string and quantum field theory are combined with powerful techniques from complex and algebraic geometry, then translated into algorithms with the ultimate aim of deriving new information about Calabi-Yau manifolds. While the motivation comes from mathematical physics, the techniques are purely mathematical and the theme is that of explicit calculations. The reader is guided through the theory and provided with explicit computer code in standard software such as SageMath, Python and Mathematica to gain hands-on experience in applications of artificial intelligence to geometry. Driven by data and written in an informal style, The Calabi-Yau Landscape makes cutting-edge topics in mathematical physics, geometry and machine learning readily accessible to graduate students and beyond. The overriding ambition is to introduce some modern mathematics to the physicist, some modern physics to the mathematician, and machine learning to both.

Artificial Intelligence for Materials Science (Paperback, 1st ed. 2021): Yuan Cheng, Tian Wang, Gang Zhang Artificial Intelligence for Materials Science (Paperback, 1st ed. 2021)
Yuan Cheng, Tian Wang, Gang Zhang
R5,069 Discovery Miles 50 690 Ships in 10 - 15 working days

Machine learning methods have lowered the cost of exploring new structures of unknown compounds, and can be used to predict reasonable expectations and subsequently validated by experimental results. As new insights and several elaborative tools have been developed for materials science and engineering in recent years, it is an appropriate time to present a book covering recent progress in this field. Searchable and interactive databases can promote research on emerging materials. Recently, databases containing a large number of high-quality materials properties for new advanced materials discovery have been developed. These approaches are set to make a significant impact on human life and, with numerous commercial developments emerging, will become a major academic topic in the coming years. This authoritative and comprehensive book will be of interest to both existing researchers in this field as well as others in the materials science community who wish to take advantage of these powerful techniques. The book offers a global spread of authors, from USA, Canada, UK, Japan, France, Russia, China and Singapore, who are all world recognized experts in their separate areas. With content relevant to both academic and commercial points of view, and offering an accessible overview of recent progress and potential future directions, the book will interest graduate students, postgraduate researchers, and consultants and industrial engineers.

Building Machine Learning Pipelines (Paperback): Hannes Hapke Building Machine Learning Pipelines (Paperback)
Hannes Hapke; Contributions by Catherine Nelson
R1,552 R1,361 Discovery Miles 13 610 Save R191 (12%) Ships in 12 - 19 working days

Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques

Machine Learning for Oracle Database Professionals - Deploying Model-Driven Applications and Automation Pipelines (Paperback,... Machine Learning for Oracle Database Professionals - Deploying Model-Driven Applications and Automation Pipelines (Paperback, 1st ed.)
Heli Helskyaho, Jean Yu, Kai Yu
R1,645 R1,343 Discovery Miles 13 430 Save R302 (18%) Ships in 10 - 15 working days

Database developers and administrators will use this book to learn how to deploy machine learning models in Oracle Database and in Oracle's Autonomous Database cloud offering. The book covers the technologies that make up the Oracle Machine Learning (OML) platform, including OML4SQL, OML Notebooks, OML4R, and OML4Py. The book focuses on Oracle Machine Learning as part of the Oracle Autonomous Database collaborative environment. Also covered are advanced topics such as delivery and automation pipelines. Throughout the book you will find practical details and hand-on examples showing you how to implement machine learning and automate deployment of machine learning. Discussion around the examples helps you gain a conceptual understanding of machine learning. Important concepts discussed include the methods involved, the algorithms to choose from, and mechanisms for process and deployment. Seasoned database professionals looking to make the leap into machine learning as a growth path will find much to like in this book as it helps you step up and use your current knowledge of Oracle Database to transition into providing machine learning solutions. What You Will Learn Use the Oracle Machine Learning (OML) Notebooks for data visualization and machine learning model building and evaluation Understand Oracle offerings for machine learning Develop machine learning with Oracle database using the built-in machine learning packages Develop and deploy machine learning models using OML4SQL and OML4R Leverage the Oracle Autonomous Database and its collaborative environment for Oracle Machine Learning Develop and deploy machine learning projects in Oracle Autonomous Database Build an automated pipeline that can detect and handle changes in data/model performance Who This Book Is For Database developers and administrators who want to learn about machine learning, developers who want to build models and applications using Oracle Database's built-in machine learning feature set, and administrators tasked with supporting applications on Oracle Database that make use of the Oracle Machine Learning feature set

Welding and Cutting Case Studies with Supervised Machine Learning (Paperback, 1st ed. 2020): S.Arungalai Vendan, Rajeev Kamal,... Welding and Cutting Case Studies with Supervised Machine Learning (Paperback, 1st ed. 2020)
S.Arungalai Vendan, Rajeev Kamal, Abhinav Karan, Liang Gao, Xiaodong Niu, …
R2,863 Discovery Miles 28 630 Ships in 10 - 15 working days

This book presents machine learning as a set of pre-requisites, co-requisites, and post-requisites, focusing on mathematical concepts and engineering applications in advanced welding and cutting processes. It describes a number of advanced welding and cutting processes and then assesses the parametrical interdependencies of two entities, namely the data analysis and data visualization techniques, which form the core of machine learning. Subsequently, it discusses supervised learning, highlighting Python libraries such as NumPy, Pandas and Scikit Learn programming. It also includes case studies that employ machine learning for manufacturing processes in the engineering domain. The book not only provides beginners with an introduction to machine learning for applied sciences, enabling them to address global competitiveness and work on real-time technical challenges, it is also a valuable resource for scholars with domain knowledge.

Machine Learning for Robotics Applications (Paperback, 1st ed. 2021): Monica Bianchini, Milan Simic, Ankush Ghosh, Rabindra... Machine Learning for Robotics Applications (Paperback, 1st ed. 2021)
Monica Bianchini, Milan Simic, Ankush Ghosh, Rabindra Nath Shaw
R5,052 Discovery Miles 50 520 Ships in 10 - 15 working days

Machine learning has become one of the most prevalent topics in recent years. The application of machine learning we see today is a tip of the iceberg. The machine learning revolution has just started to roll out. It is becoming an integral part of all modern electronic devices. Applications in automation areas like automotive, security and surveillance, augmented reality, smart home, retail automation and healthcare are few of them. Robotics is also rising to dominate the automated world. The future applications of machine learning in the robotics area are still undiscovered to the common readers. We are, therefore, putting an effort to write this edited book on the future applications of machine learning on robotics where several applications have been included in separate chapters. The content of the book is technical. It has been tried to cover all possible application areas of Robotics using machine learning. This book will provide the future vision on the unexplored areas of applications of Robotics using machine learning. The ideas to be presented in this book are backed up by original research results. The chapter provided here in-depth look with all necessary theory and mathematical calculations. It will be perfect for laymen and developers as it will combine both advanced and introductory material to form an argument for what machine learning could achieve in the future. It will provide a vision on future areas of application and their approach in detail. Therefore, this book will be immensely beneficial for the academicians, researchers and industry project managers to develop their new project and thereby beneficial for mankind. Original research and review works with model and build Robotics applications using Machine learning are included as chapters in this book.

Proceedings of International Conference in Mechanical and Energy Technology - ICMET 2019, India (Paperback, 1st ed. 2020):... Proceedings of International Conference in Mechanical and Energy Technology - ICMET 2019, India (Paperback, 1st ed. 2020)
Sanjay Yadav, D.B. Singh, P. K. Arora, Harish Kumar
R4,509 Discovery Miles 45 090 Ships in 10 - 15 working days

This book presents selected peer-reviewed papers from the International Conference on Mechanical and Energy Technologies, which was held on 7-8 November 2019 at Galgotias College of Engineering and Technology, Greater Noida, India. The book reports on the latest developments in the field of mechanical and energy technology in contributions prepared by experts from academia and industry. The broad range of topics covered includes aerodynamics and fluid mechanics, artificial intelligence, nonmaterial and nonmanufacturing technologies, rapid manufacturing technologies and prototyping, remanufacturing, renewable energies technologies, metrology and computer-aided inspection, etc. Accordingly, the book offers a valuable resource for researchers in various fields, especially mechanical and industrial engineering, and energy technologies.

Applications of Artificial Intelligence and Machine Learning - Select Proceedings of ICAAAIML 2020 (Hardcover, 1st ed. 2021):... Applications of Artificial Intelligence and Machine Learning - Select Proceedings of ICAAAIML 2020 (Hardcover, 1st ed. 2021)
Ankur Choudhary, Arun Prakash Agrawal, Rajasvaran Logeswaran, Bhuvan Unhelkar
R5,199 Discovery Miles 51 990 Ships in 12 - 19 working days

The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning - ICAAAIML 2020. The book covers research in artificial intelligence, machine learning, and deep learning applications in healthcare, agriculture, business, and security. This volume contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book will be a valuable resource for students, academics, and practitioners in the industry working on AI applications.

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