Artificial Intelligence, Machine Learning, and Deep Learning

Artificial Intelligence, Machine Learning, and Deep Learning
Author: Oswald Campesato
Publisher: Mercury Learning and Information
Total Pages: 306
Release: 2020-01-23
Genre: Computers
ISBN: 1683924665

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This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine learning classifiers such as logistic regression, k-NN, decision trees, random forests, and SVMs. Next, the book covers deep learning architectures such as CNNs, RNNs, LSTMs, and auto encoders. Keras-based code samples are included to supplement the theoretical discussion. In addition, this book contains appendices for Keras, TensorFlow 2, and Pandas. Features: Covers an introduction to programming concepts related to AI, machine learning, and deep learning Includes material on Keras, TensorFlow2 and Pandas

Artificial Intelligence and Deep Learning in Pathology

Artificial Intelligence and Deep Learning in Pathology
Author: Stanley Cohen
Publisher: Elsevier Health Sciences
Total Pages: 290
Release: 2020-06-02
Genre: Medical
ISBN: 0323675379

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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.

Artificial Intelligence Driven by Machine Learning and Deep Learning

Artificial Intelligence Driven by Machine Learning and Deep Learning
Author: Bahman Zohuri
Publisher: Nova Science Publishers
Total Pages: 455
Release: 2020
Genre: Computers
ISBN: 9781536183672

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"The future of any business from banking, e-commerce, real estate, homeland security, healthcare, marketing, the stock market, manufacturing, education, retail to government organizations depends on the data and analytics capabilities that are built and scaled. The speed of change in technology in recent years has been a real challenge for all businesses. To manage that, a significant number of organizations are exploring the BigData (BD) infrastructure that helps them to take advantage of new opportunities while saving costs. Timely transformation of information is also critical for the survivability of an organization. Having the right information at the right time will enhance not only the knowledge of stakeholders within an organization but also providing them with a tool to make the right decision at the right moment. It is no longer enough to rely on a sampling of information about the organizations' customers. The decision-makers need to get vital insights into the customers' actual behavior, which requires enormous volumes of data to be processed. We believe that Big Data infrastructure is the key to successful Artificial Intelligence (AI) deployments and accurate, unbiased real-time insights. Big data solutions have a direct impact and changing the way the organization needs to work with help from AI and its components ML and DL. In this article, we discuss these topics"--

Deep Learning

Deep Learning
Author: Ian Goodfellow
Publisher: MIT Press
Total Pages: 801
Release: 2016-11-10
Genre: Computers
ISBN: 0262337371

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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.

Machine Learning and Deep Learning in Real-Time Applications

Machine Learning and Deep Learning in Real-Time Applications
Author: Mahrishi, Mehul
Publisher: IGI Global
Total Pages: 344
Release: 2020-04-24
Genre: Computers
ISBN: 1799830977

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Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.

Machine Learning and Artificial Intelligence

Machine Learning and Artificial Intelligence
Author: Ameet V Joshi
Publisher: Springer Nature
Total Pages: 261
Release: 2019-09-24
Genre: Technology & Engineering
ISBN: 3030266222

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This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The forth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. Presents a full reference to artificial intelligence and machine learning techniques - in theory and application; Provides a guide to AI and ML with minimal use of mathematics to make the topics more intuitive and accessible; Connects all ML and AI techniques to applications and introduces implementations.

Deep Learning Applications in Medical Imaging

Deep Learning Applications in Medical Imaging
Author: Saxena, Sanjay
Publisher: IGI Global
Total Pages: 274
Release: 2020-10-16
Genre: Medical
ISBN: 1799850722

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Before the modern age of medicine, the chance of surviving a terminal disease such as cancer was minimal at best. After embracing the age of computer-aided medical analysis technologies, however, detecting and preventing individuals from contracting a variety of life-threatening diseases has led to a greater survival percentage and increased the development of algorithmic technologies in healthcare. Deep Learning Applications in Medical Imaging is a pivotal reference source that provides vital research on the application of generating pictorial depictions of the interior of a body for medical intervention and clinical analysis. While highlighting topics such as artificial neural networks, disease prediction, and healthcare analysis, this publication explores image acquisition and pattern recognition as well as the methods of treatment and care. This book is ideally designed for diagnosticians, medical imaging specialists, healthcare professionals, physicians, medical researchers, academicians, and students.

AI, Machine Learning and Deep Learning

AI, Machine Learning and Deep Learning
Author: Fei Hu
Publisher: CRC Press
Total Pages: 420
Release: 2023-06-05
Genre: Computers
ISBN: 1000878899

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Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use. While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security). Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects: This is the first book to explain various practical attacks and countermeasures to AI systems Both quantitative math models and practical security implementations are provided It covers both "securing the AI system itself" and "using AI to achieve security" It covers all the advanced AI attacks and threats with detailed attack models It provides multiple solution spaces to the security and privacy issues in AI tools The differences among ML and DL security and privacy issues are explained Many practical security applications are covered

Deep Learning Illustrated

Deep Learning Illustrated
Author: Jon Krohn
Publisher: Addison-Wesley Professional
Total Pages: 725
Release: 2019-08-05
Genre: Computers
ISBN: 0135121728

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"The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

WIPO Technology Trends 2019 - Artificial Intelligence

WIPO Technology Trends 2019 - Artificial Intelligence
Author: World Intellectual Property Organization
Publisher: WIPO
Total Pages: 156
Release: 2019-01-21
Genre: Law
ISBN: 9280530070

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The first report in a new flagship series, WIPO Technology Trends, aims to shed light on the trends in innovation in artificial intelligence since the field first developed in the 1950s.