Artificial Intelligence And Machine Learning Principles And Applications
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| Author | : Dr. Shashi Tanwar |
| Publisher | : Academic Guru Publishing House |
| Total Pages | : 258 |
| Release | : 2024-08-07 |
| Genre | : Study Aids |
| ISBN | : 8197857164 |
“Artificial Intelligence and Machine Learning – Principles and Applications” is a comprehensive guide that delves into the core concepts, methodologies, and practical implementations of AI and machine learning. Authored with clarity and expertise, it serves as an indispensable resource for both beginners and seasoned professionals in the field. The book begins by elucidating the fundamental principles underlying artificial intelligence and machine learning, providing readers with a solid foundation to build upon. From there, it progresses into more advanced topics, covering a wide range of algorithms, techniques, and applications across various domains. Readers are guided through the intricacies of machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and deep learning. Each concept is accompanied by illustrative examples and offers a hands-on approach to learning. Furthermore, the book explores the ethical and societal implications of AI and machine learning, prompting readers to consider the broader implications of their work. It discusses issues such as bias, fairness, privacy, and transparency, encouraging a responsible approach to AI development and deployment. One of the standout features of “Artificial Intelligence and Machine Learning – Principles and Applications” is its emphasis on practical applications. It provides insights into how AI and machine learning techniques can be leveraged to solve complex problems in areas such as healthcare, finance, marketing, and beyond. Overall, this book serves as an invaluable resource for anyone looking to gain a comprehensive understanding of artificial intelligence and machine learning, offering both theoretical insights and practical guidance for real-world implementation.
| Author | : PETER. WLODARCZAK |
| Publisher | : CRC Press |
| Total Pages | : 188 |
| Release | : 2021-06-30 |
| Genre | : |
| ISBN | : 9781032086774 |
In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have provided groundbreaking outcomes in fields such as multimedia mining or voice recognition. Machine learning is now used in virtually every domain and deep learning algorithms are present in many devices such as smartphones, cars, drones, healthcare equipment, or smart home devices. The Internet, cloud computing and the Internet of Things produce a tsunami of data and machine learning provides the methods to effectively analyze the data and discover actionable knowledge. This book describes the most common machine learning techniques such as Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural networks. It first gives an introduction into the principles of machine learning. It then covers the basic methods including the mathematical foundations. The biggest part of the book provides common machine learning algorithms and their applications. Finally, the book gives an outlook into some of the future developments and possible new research areas of machine learning and artificial intelligence in general. This book is meant to be an introduction into machine learning. It does not require prior knowledge in this area. It covers some of the basic mathematical principle but intends to be understandable even without a background in mathematics. It can be read chapter wise and intends to be comprehensible, even when not starting in the beginning. Finally, it also intends to be a reference book. Key Features: Describes real world problems that can be solved using Machine Learning Provides methods for directly applying Machine Learning techniques to concrete real world problems Demonstrates how to apply Machine Learning techniques using different frameworks such as TensorFlow, MALLET, R
| Author | : Mahmoud Hassaballah |
| Publisher | : CRC Press |
| Total Pages | : 275 |
| Release | : 2020-03-23 |
| Genre | : Computers |
| ISBN | : 1351003801 |
Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.
| Author | : Stanley Cohen |
| Publisher | : Elsevier Health Sciences |
| Total Pages | : 290 |
| Release | : 2020-06-02 |
| Genre | : Medical |
| ISBN | : 0323675379 |
Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience. - Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. - Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. - Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.
| Author | : Zsolt Nagy |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 330 |
| Release | : 2018-12-12 |
| Genre | : Computers |
| ISBN | : 1789809207 |
Create AI applications in Python and lay the foundations for your career in data science Key FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail with interesting examplesMaster core AI concepts with engaging activitiesBook Description Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills! What you will learnUnderstand the importance, principles, and fields of AIImplement basic artificial intelligence concepts with PythonApply regression and classification concepts to real-world problemsPerform predictive analysis using decision trees and random forestsCarry out clustering using the k-means and mean shift algorithmsUnderstand the fundamentals of deep learning via practical examplesWho this book is for Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it’s recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).
| Author | : M. Gopal |
| Publisher | : McGraw-Hill Education |
| Total Pages | : 656 |
| Release | : 2019-06-05 |
| Genre | : Technology & Engineering |
| ISBN | : 9781260456844 |
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Cutting-edge machine learning principles, practices, and applications This comprehensive textbook explores the theoretical under¬pinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning shows, step by step, how to conceptualize problems, accurately represent data, select and tune algorithms, interpret and analyze results, and make informed strategic decisions. Presented in a non-rigorous mathematical style, the book covers a broad array of machine learning topics with special emphasis on methods that have been profitably employed. Coverage includes: •Supervised learning•Statistical learning•Learning with support vector machines (SVM)•Learning with neural networks (NN)•Fuzzy inference systems•Data clustering•Data transformations•Decision tree learning•Business intelligence•Data mining•And much more
| Author | : Dr.Vemuri Sudarsan Rao |
| Publisher | : Leilani Katie Publication |
| Total Pages | : 206 |
| Release | : 2024-09-05 |
| Genre | : Computers |
| ISBN | : 9363486850 |
Dr.Vemuri Sudarsan Rao, Professor & Head, Department of Computer Science & Engineering, Sri Chaitanya Institute of Technology and Research (SCIT), Khammam, Telangana, India. Mr.A.Satish, Associate Professor, Department of Computer Science & Engineering, Sri Chaitanya Institute of Technology and Research (SCIT), Khammam, Telangana, India. Mr.BBLV Prasad, Associate Professor, Department of Computer Science & Engineering, Sri Chaitanya Institute of Technology and Research (SCIT), Khammam, Telangana, India.
| Author | : Adam Bohr |
| Publisher | : Academic Press |
| Total Pages | : 385 |
| Release | : 2020-06-21 |
| Genre | : Computers |
| ISBN | : 0128184396 |
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
| Author | : M. Arif Wani |
| Publisher | : Springer |
| Total Pages | : 300 |
| Release | : 2020-12-14 |
| Genre | : Technology & Engineering |
| ISBN | : 9789811567582 |
This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.
| Author | : Daniel A. Hashimoto |
| Publisher | : McGraw Hill Professional |
| Total Pages | : 432 |
| Release | : 2021-03-08 |
| Genre | : Medical |
| ISBN | : 1260452743 |
Build a solid foundation in surgical AI with this engaging, comprehensive guide for AI novices Machine learning, neural networks, and computer vision in surgical education, practice, and research will soon be de rigueur. Written for surgeons without a background in math or computer science, Artificial Intelligence in Surgery provides everything you need to evaluate new technologies and make the right decisions about bringing AI into your practice. Comprehensive and easy to understand, this first-of-its-kind resource illustrates the use of AI in surgery through real-life examples. It covers the issues most relevant to your practice, including: Neural Networks and Deep Learning Natural Language Processing Computer Vision Surgical Education and Simulation Preoperative Risk Stratification Intraoperative Video Analysis OR Black Box and Tracking of Intraoperative Events Artificial Intelligence and Robotic Surgery Natural Language Processing for Clinical Documentation Leveraging Artificial Intelligence in the EMR Ethical Implications of Artificial Intelligence in Surgery Artificial Intelligence and Health Policy Assessing Strengths and Weaknesses of Artificial Intelligence Research Finally, the appendix includes a detailed glossary of terms and important learning resources and techniques―all of which helps you interpret claims made by studies or companies using AI.
