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Showing 20 of 987 academic articles

Deep Learning in Neuroradiology

Greg Zaharchuk, Enhao Gong, Max Wintermark, Daniel L. Rubin et al. • American Journal of Neuroradiology (2018)

Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applic...

OPENALEX cc-by DOI: https://doi.org/10.3174/ajnr.a5543 PMC: 7410723

Deep Learning for Health Informatics

Daniele Ravì, Charence Wong, Fani Deligianni, Melissa Berthelot et al. • IEEE Journal of Biomedical and Health Informatics (2016)

With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on mac...

OPENALEX cc-by DOI: https://doi.org/10.1109/jbhi.2016.2636665

Deep learning for computational biology

Christof Angermueller, Tanel Pärnamaa, Leopold Parts, Oliver Stegle • Molecular Systems Biology (2016)

Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventiona...

OPENALEX other-oa DOI: https://doi.org/10.15252/msb.20156651 PMC: 4965871

Dive into Deep Learning

Aston Zhang, Zachary C. Lipton, Li Mu, Alexander Johannes Smola • arXiv (Cornell University) (2021)

This open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and...

OPENALEX cc-by DOI: https://doi.org/10.48550/arxiv.2106.11342

Deep Learning in Chemistry

Adam C. Mater, Michelle L. Coote • Journal of Chemical Information and Modeling (2019)

Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns repres...

OPENALEX DOI: https://doi.org/10.1021/acs.jcim.9b00266

Deep Learning

Hao Xing, Guigang Zhang, Shang Ma • International Journal of Semantic Computing (2016)

Deep learning is a branch of machine learning that tries to model high-level abstractions of data using multiple layers of neurons consisting of complex structures or non-liner transformations. With the increase of the amount of data and th...

OPENALEX DOI: https://doi.org/10.1142/s1793351x16500045

A Survey on Deep Learning

Samira Pouyanfar, Saad B Sadiq, Yilin Yan, Haiman Tian et al. • ACM Computing Surveys (2018)

The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler d...

OPENALEX DOI: https://doi.org/10.1145/3234150

Deep Learning

Ian Goodfellow, Yoshua Bengio, Aaron C. Courville • The MIT Press eBooks (2016)

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 c...

OPENALEX DOI: https://doi.org/10.1016/b978-0-12-804291-5.00010-6

Data Mining and Machine Learning

Mohammed Javeed Zaki, Wagner Meira • Cambridge University Press eBooks (2020)

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business an...

OPENALEX DOI: https://doi.org/10.1017/9781108564175

Machine Learning in Nutrition Research

Daniel Kirk, E.J. Kok, Michele Tufano, Bedir Teki̇nerdoğan et al. • Advances in Nutrition (2022)

Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and ...

OPENALEX cc-by-nc DOI: https://doi.org/10.1093/advances/nmac103 PMC: 9776646

Machine Learning

Jason Bell • Wiley eBooks (2014)

Emotion recognition is an important research topic.Physiological signals seem to be an appropriate way for emotion recognition and specific sensors are required to collect these data.Therefore, laboratory sensors are commonly used while the...

OPENALEX DOI: https://doi.org/10.1002/9781119183464

Foundations of Machine Learning

Vishal Kumar Goar, Nagendra Singh Yadav • Advances in business information systems and analytics book series (2024)

This chapter focuses on providing a complete grasp of the foundations of machine learning (ML). Machine learning is a rapidly evolving domain with wide-ranging applications, from natural language processing to autonomous vehicles. This chap...

OPENALEX DOI: https://doi.org/10.4018/979-8-3693-1598-9.ch002

Fairness in Machine Learning: A Survey

Simon J. Caton, Christian Stefan Haas • ACM Computing Surveys (2023)

When Machine Learning technologies are used in contexts that affect citizens, companies as well as researchers need to be confident that there will not be any unexpected social implications, such as bias towards gender, ethnicity, and/or pe...

OPENALEX cc-by DOI: https://doi.org/10.1145/3616865