Artificial Intelligence and Deep Learning in Pathology

Artificial Intelligence and Deep Learning in Pathology
Author: Stanley Cohen
Publsiher: Elsevier
Total Pages: 250
Release: 2020-06
ISBN 10: 9780323675383
ISBN 13: 0323675387
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Deep Learning in Pathology Book Review:

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 and Deep Learning in Pathology

Artificial Intelligence and Deep Learning in Pathology
Author: Stanley Cohen
Publsiher: Elsevier Health Sciences
Total Pages: 288
Release: 2020-06-02
ISBN 10: 0323675379
ISBN 13: 9780323675376
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Deep Learning in Pathology Book Review:

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 and Machine Learning for Digital Pathology

Artificial Intelligence and Machine Learning for Digital Pathology
Author: Andreas Holzinger,Randy Goebel,Michael Mengel,Heimo Müller
Publsiher: Springer Nature
Total Pages: 341
Release: 2020
ISBN 10: 3030504026
ISBN 13: 9783030504021
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Machine Learning for Digital Pathology Book Review:

Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ''fit-for-purpose samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions.

Artificial Intelligence Applications in Human Pathology

Artificial Intelligence Applications in Human Pathology
Author: Ralf Huss,Michael Grunkin
Publsiher: Unknown
Total Pages: 135
Release: 2022-01-22
ISBN 10: 9781800611382
ISBN 13: 1800611382
Language: EN, FR, DE, ES & NL

Artificial Intelligence Applications in Human Pathology Book Review:

Artificial Intelligence Applications in Human Pathology deals with the latest topics in biomedical research and clinical cancer diagnostics. With chapters provided by true international experts in the field, this book gives real examples of the implementation of AI and machine learning in human pathology.Advances in machine learning and AI in general have propelled computational and general pathology research. Today, computer systems approach the diagnostic levels achieved by humans for certain well-defined tasks in pathology. At the same time, pathologists are faced with an increased workload both quantitatively (numbers of cases) and qualitatively (the amount of work per case, with increasing treatment options and the type of data delivered by pathologists also expected to become more fine-grained). AI will support and leverage mathematical tools and implement data-driven methods as a center for data interpretation in modern tissue diagnosis and pathology. Digital or computational pathology will also foster the training of future computational pathologists, those with both pathology and non-pathology backgrounds, who will eventually decide that AI-based pathology will serve as an indispensable hub for data-related research in a global health care system.Some of the specific topics explored within include an introduction to DL as applied to Pathology, Standardized Tissue Sampling for Automated Analysis, integrating Computational Pathology into Histopathology workflows. Readers will also find examples of specific techniques applied to specific diseases that will aid their research and treatments including but not limited to; Tissue Cartography for Colorectal Cancer, Ki-67 Measurements in Breast Cancer, and Light-Sheet Microscopy as applied to Virtual Histology.The key role for pathologists in tissue diagnostics will prevail and even expand through interdisciplinary work and the intuitive use of an advanced and interoperating (AI-supported) pathology workflow delivering novel and complex features that will serve the understanding of individual diseases and of course the patient.

Digital Pathology

Digital Pathology
Author: Liron Pantanowitz,Anil V. Parwani
Publsiher: Unknown
Total Pages: 304
Release: 2017
ISBN 10: 9780891896104
ISBN 13: 0891896104
Language: EN, FR, DE, ES & NL

Digital Pathology Book Review:

The definitive, complete reference of digital pathology! An extraordinarily comprehensive and complete book for individuals with anything from minimal knowledge to deep, accomplished experience in digital pathology. Easy to read and plainly written, Digital Pathology examines the history and technological evolution of digital pathology, from the birth of scanning technology and telepathology to three-dimensional imaging on large multi-touch displays and computer aided diagnosis. A must-have book for anyone wishing to learn more about and work in this exciting and critical information environment including pathologists, laboratory professionals, students and any other medical practitioners with a particular interest in the history and future of digital pathology. It can also be a useful reference for anyone, medical or non-medical, who have an interest in learning more about the field. Digital pathology is truly a game changer, and this book is a crucial tool for anyone wishing to know more. Subjects discussed in depth include: Static digital imaging; basics and clinical use. Digital imaging processes. Telepathology. While slide imaging. Clinical applications of whole slide imaging. Digital pathology for educational, quality improvement, research and other settings. Forensic digital imaging.

Artificial Intelligence for Data Driven Medical Diagnosis

Artificial Intelligence for Data Driven Medical Diagnosis
Author: Deepak Gupta,Utku Kose,Bao Le Nguyen,Siddhartha Bhattacharyya
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 326
Release: 2021-01-28
ISBN 10: 3110668386
ISBN 13: 9783110668384
Language: EN, FR, DE, ES & NL

Artificial Intelligence for Data Driven Medical Diagnosis Book Review:

This book collects research works of data-driven medical diagnosis done via Artificial Intelligence based solutions, such as Machine Learning, Deep Learning and Intelligent Optimization. Physical devices powered with Artificial Intelligence are gaining importance in diagnosis and healthcare. Medical data from different sources can also be analyzed via Artificial Intelligence techniques for more effective results.

Medical Imaging

Medical Imaging
Author: K.C. Santosh,Sameer Antani,DS Guru,Nilanjan Dey
Publsiher: CRC Press
Total Pages: 238
Release: 2019-08-20
ISBN 10: 0429642490
ISBN 13: 9780429642494
Language: EN, FR, DE, ES & NL

Medical Imaging Book Review:

The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.

Machine Learning and the Internet of Medical Things in Healthcare

Machine Learning and the Internet of Medical Things in Healthcare
Author: Krishna Kant Singh,Mohamed Elhoseny,Akansha Singh,Ahmed A. Elngar
Publsiher: Academic Press
Total Pages: 290
Release: 2021-04-26
ISBN 10: 012823217X
ISBN 13: 9780128232170
Language: EN, FR, DE, ES & NL

Machine Learning and the Internet of Medical Things in Healthcare Book Review:

Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide. The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks. Provides an introduction to the Internet of Medical Things through the principles and applications of machine learning Explains the functions and applications of machine learning in various applications such as ultrasound imaging, biomedical signal processing, robotics, and biomechatronics Includes coverage of the evolution of healthcare applications with machine learning, including Clinical Decision Support Systems, artificial intelligence in biomedical engineering, and AI-enabled connected health informatics, supported by real-world case studies

Digital Pathology

Digital Pathology
Author: Constantino Carlos Reyes-Aldasoro,Andrew Janowczyk,Mitko Veta,Peter Bankhead,Korsuk Sirinukunwattana
Publsiher: Springer
Total Pages: 192
Release: 2019-07-03
ISBN 10: 3030239373
ISBN 13: 9783030239374
Language: EN, FR, DE, ES & NL

Digital Pathology Book Review:

This book constitutes the refereed proceedings of the 15th European Congress on Digital Pathology, ECDP 2019, held in Warwick, UK in April 2019. The 21 full papers presented in this volume were carefully reviewed and selected from 30 submissions. The congress theme will be Accelerating Clinical Deployment, with a focus on computational pathology and leveraging the power of big data and artificial intelligence to bridge the gaps between research, development, and clinical uptake.

Deep Medicine

Deep Medicine
Author: Eric Topol
Publsiher: Basic Books
Total Pages: 400
Release: 2019-03-12
ISBN 10: 1541644646
ISBN 13: 9781541644649
Language: EN, FR, DE, ES & NL

Deep Medicine Book Review:

One of America's top doctors reveals how AI will empower physicians and revolutionize patient care Medicine has become inhuman, to disastrous effect. The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard. Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved.

Whole Slide Imaging

Whole Slide Imaging
Author: Anil V. Parwani
Publsiher: Springer Nature
Total Pages: 242
Release: 2021-10-29
ISBN 10: 3030833321
ISBN 13: 9783030833329
Language: EN, FR, DE, ES & NL

Whole Slide Imaging Book Review:

This book provides up-to-date and practical knowledge in all aspects of whole slide imaging (WSI) by experts in the field. This includes a historical perspective on the evolution of this technology, technical aspects of making a great whole slide image, the various applications of whole slide imaging and future applications using WSI for computer-aided diagnosis The goal is to provide practical knowledge and address knowledge gaps in this emerging field. This book is unique because it addresses an emerging area in pathology for which currently there is only limited information about the practical aspects of deploying this technology. For example, there are no established selection criteria for choosing new scanners and a knowledge base with the key information. The authors of the various chapters have years of real-world experience in selecting and implementing WSI solutions in various aspects of pathology practice. This text also discusses practical tips and pearls to address the selection of a WSI vendor, technology details, implementing this technology and provide an overview of its everyday uses in all areas of pathology. Chapters include important information on how to integrate digital slides with laboratory information system and how to streamline the “digital workflow” with the intent of saving time, saving money, reducing errors, improving efficiency and accuracy, and ultimately benefiting patient outcomes. Whole Slide Imaging: Current Applications and Future Directions is designed to present a comprehensive and state-of the-art approach to WSI within the broad area of digital pathology. It aims to give the readers a look at WSI with a deeper lens and also envision the future of pathology imaging as it pertains to WSI and associated digital innovations.

Artificial Intelligence in Medical Imaging

Artificial Intelligence in Medical Imaging
Author: Erik R. Ranschaert,Sergey Morozov,Paul R. Algra
Publsiher: Springer
Total Pages: 373
Release: 2019-01-29
ISBN 10: 3319948784
ISBN 13: 9783319948782
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Medical Imaging Book Review:

This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

Emerging Trends in ICT for Sustainable Development

Emerging Trends in ICT for Sustainable Development
Author: Mohamed Ben Ahmed,Sehl Mellouli,Luis Braganca,Boudhir Anouar Abdelhakim,Kwintiana Ane Bernadetta
Publsiher: Springer Nature
Total Pages: 407
Release: 2021-01-23
ISBN 10: 3030534405
ISBN 13: 9783030534400
Language: EN, FR, DE, ES & NL

Emerging Trends in ICT for Sustainable Development Book Review:

This book features original research and recent advances in ICT fields related to sustainable development. Based the International Conference on Networks, Intelligent systems, Computing & Environmental Informatics for Sustainable Development, held in Marrakech in April 2020, it features peer-reviewed chapters authored by prominent researchers from around the globe. As such it is an invaluable resource for courses in computer science, electrical engineering and urban sciences for sustainable development. This book covered topics including • Green Networks • Artificial Intelligence for Sustainability• Environment Informatics• Computing Technologies

Artificial Intelligence in Design 96

Artificial Intelligence in Design    96
Author: John S. Gero,Fay Sudweeks
Publsiher: Springer Science & Business Media
Total Pages: 782
Release: 2012-12-06
ISBN 10: 9400902794
ISBN 13: 9789400902794
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Design 96 Book Review:

Change is one of the most significant parameters in our society. Designers are amongst the primary change agents for any society. As a consequence design is an important research topic in engineering and architecture and related disciplines, since design is not only a means of change but is also one of the keystones to economic competitiveness and the fundamental precursor to manufacturing. The development of computational models founded on the artificial intelligence paradigm has provided an impetus for much of current design research -both computational and cognitive. These forms of design research have only been carried out in the last decade or so and in the temporal sense they are still immature. Notwithstanding this immaturity, noticeable advances have been made both in extending our understanding of design and in developing tools based on that understanding. Whilst many researchers in the field of artificial intelligence in design utilise ideas about how humans design as one source of concepts there is normally no attempt to model human designers. Rather the results of the research presented in this volume demonstrate approaches to increasing our understanding of design as a process.

Machine Learning in Cardiovascular Medicine

Machine Learning in Cardiovascular Medicine
Author: Subhi J. Al'Aref,Gurpreet Singh,Lohendran Baskaran,Dimitri Metaxas
Publsiher: Academic Press
Total Pages: 454
Release: 2020-11-20
ISBN 10: 0128202742
ISBN 13: 9780128202746
Language: EN, FR, DE, ES & NL

Machine Learning in Cardiovascular Medicine Book Review:

Machine Learning in Cardiovascular Medicine addresses the ever-expanding applications of artificial intelligence (AI), specifically machine learning (ML), in healthcare and within cardiovascular medicine. The book focuses on emphasizing ML for biomedical applications and provides a comprehensive summary of the past and present of AI, basics of ML, and clinical applications of ML within cardiovascular medicine for predictive analytics and precision medicine. It helps readers understand how ML works along with its limitations and strengths, such that they can could harness its computational power to streamline workflow and improve patient care. It is suitable for both clinicians and engineers; providing a template for clinicians to understand areas of application of machine learning within cardiovascular research; and assist computer scientists and engineers in evaluating current and future impact of machine learning on cardiovascular medicine. Provides an overview of machine learning, both for a clinical and engineering audience Summarize recent advances in both cardiovascular medicine and artificial intelligence Discusses the advantages of using machine learning for outcomes research and image processing Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach

Liquid Biopsy

Liquid Biopsy
Author: Ilze Strumfa,Janis Gardovskis
Publsiher: BoD – Books on Demand
Total Pages: 158
Release: 2019-07-10
ISBN 10: 183881129X
ISBN 13: 9781838811297
Language: EN, FR, DE, ES & NL

Liquid Biopsy Book Review:

Reliable diagnosis is the cornerstone, starting point, and prerequisite of successful treatment. Therefore, development of innovative diagnostic technologies represents a hot topic in medical research. Liquid biopsy is a novel, minimally invasive laboratory evaluation concept for diagnostic, prognostic, and predictive testing, as well as dynamic monitoring of treatment or disease course. To achieve these goals, a multitude of specific, targeted tests can be performed to detect free nucleic acids, exosomes, microRNAs, tumor-educated platelets, and whole cells of tumor or fetal origin in different biological fluids, including blood, urine, cerebrospinal fluid, and others. Although tissue biopsy has long been considered the gold standard of diagnostics, especially regarding malignant tumors, liquid biopsy has the advantages of a non-invasive approach and thus low risk of complications. It is technically feasible even in serious general status or if tumors or metastases are not easily accessible using conventional tissue biopsy. The testing is fast, exact, and can be repeated to ensure real-time follow-up. In contrast to classic tumor markers, liquid biopsy is distinguished by high specificity at genomic, proteomic, and cellular levels. It is expected to equal and exceed the diagnostic value of tissue biopsy. The field of liquid biopsies is developing rapidly regarding the selection of targets, technological improvements, and quality assessment. This book, written by a global team of recognized scientists, comprises state-of-the-art reviews on the current knowledge and advances in the technologies and software for liquid biopsy. Examples of practical application of liquid biopsy to evaluate thyroid cancer, multiple myeloma, etc. are discussed as well. The book is intended to serve as a reference for scientists and clinicians interested in the development and practical implementation of liquid biopsy.

Deep Learning in Healthcare

Deep Learning in Healthcare
Author: Yen-Wei Chen,Lakhmi C. Jain
Publsiher: Springer Nature
Total Pages: 218
Release: 2019-11-18
ISBN 10: 3030326063
ISBN 13: 9783030326067
Language: EN, FR, DE, ES & NL

Deep Learning in Healthcare Book Review:

This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data. Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.

Bioinformatics Basics

Bioinformatics Basics
Author: Lukas K. Buehler,Hooman H. Rashidi
Publsiher: CRC Press
Total Pages: 360
Release: 2005-06-23
ISBN 10: 1482292343
ISBN 13: 9781482292343
Language: EN, FR, DE, ES & NL

Bioinformatics Basics Book Review:

Every researcher in genomics and proteomics now has access to public domain databases containing literally billions of data entries. However, without the right analytical tools, and an understanding of the biological significance of the data, cataloging and interpreting the molecular evolutionary processes buried in those databases is difficult, if not impossible. The first editon of Bioinformatics Basics: Applications in Biological Science and Medicine answered the scientific community’s need to learn about the bioinformatic tools available to them. That the book continues to be a best seller clearly demonstrates the authors’ ability to provide scientists with the understanding to apply those tools to their research. Currently, it is being used as a reference text at MIT and other prestigious institutions. Recognizing the important advances in bioinformatices since their last edition, Buehler and Rashidi have produced a completely revised and updated version of their pioneering work. To allow scientists to utilize significant databases from around the world, the authors consider some fresh approaches to data analysis while identifying computing techniques that will help them manage the massive flow of information their science requires. New to the second edition: Provides a more detailed view of the field while continuing to focus on the global concept approach that popularized the first edition. Offers the latest approaches to data analysis Introduces recent developments in genomics, microarrays, proteomics, genome mapping, and more. Adds two new sections offering insights from other experts in bioinformatics. Bioinformatics Basics is not intended to serve as a training manual for bioinformaticians. Instead, it’s designed to help the general scientific community gain a thorough understanding of what bioinformatics tools are available to them and the best ways these tools can be utilized and adapted to meet the needs of their specific interests and projects.

Natural Language Processing in Action

Natural Language Processing in Action
Author: Hannes Hapke,Cole Howard,Hobson Lane
Publsiher: Simon and Schuster
Total Pages: 544
Release: 2019-03-16
ISBN 10: 1638356890
ISBN 13: 9781638356899
Language: EN, FR, DE, ES & NL

Natural Language Processing in Action Book Review:

Summary Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Recent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before. About the Book Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions. What's inside Some sentences in this book were written by NLP! Can you guess which ones? Working with Keras, TensorFlow, gensim, and scikit-learn Rule-based and data-based NLP Scalable pipelines About the Reader This book requires a basic understanding of deep learning and intermediate Python skills. About the Author Hobson Lane, Cole Howard, and Hannes Max Hapke are experienced NLP engineers who use these techniques in production. Table of Contents PART 1 - WORDY MACHINES Packets of thought (NLP overview) Build your vocabulary (word tokenization) Math with words (TF-IDF vectors) Finding meaning in word counts (semantic analysis) PART 2 - DEEPER LEARNING (NEURAL NETWORKS) Baby steps with neural networks (perceptrons and backpropagation) Reasoning with word vectors (Word2vec) Getting words in order with convolutional neural networks (CNNs) Loopy (recurrent) neural networks (RNNs) Improving retention with long short-term memory networks Sequence-to-sequence models and attention PART 3 - GETTING REAL (REAL-WORLD NLP CHALLENGES) Information extraction (named entity extraction and question answering) Getting chatty (dialog engines) Scaling up (optimization, parallelization, and batch processing)

Towards Integrative Machine Learning and Knowledge Extraction

Towards Integrative Machine Learning and Knowledge Extraction
Author: Andreas Holzinger,Randy Goebel,Massimo Ferri,Vasile Palade
Publsiher: Springer
Total Pages: 207
Release: 2017-10-27
ISBN 10: 3319697757
ISBN 13: 9783319697758
Language: EN, FR, DE, ES & NL

Towards Integrative Machine Learning and Knowledge Extraction Book Review:

The BIRS Workshop “Advances in Interactive Knowledge Discovery and Data Mining in Complex and Big Data Sets” (15w2181), held in July 2015 in Banff, Canada, was dedicated to stimulating a cross-domain integrative machine-learning approach and appraisal of “hot topics” toward tackling the grand challenge of reaching a level of useful and useable computational intelligence with a focus on real-world problems, such as in the health domain. This encompasses learning from prior data, extracting and discovering knowledge, generalizing the results, fighting the curse of dimensionality, and ultimately disentangling the underlying explanatory factors in complex data, i.e., to make sense of data within the context of the application domain. The workshop aimed to contribute advancements in promising novel areas such as at the intersection of machine learning and topological data analysis. History has shown that most often the overlapping areas at intersections of seemingly disparate fields are key for the stimulation of new insights and further advances. This is particularly true for the extremely broad field of machine learning.