143,665 research outputs found
Beyond ECDL: basic and advanced IT skills for the new library professional
This paper reports on a new multimedia-centred ICT module, called Fundamentals of Information and Communication Technology (FICT) for Postgraduate Information and Library Studies students at the Graduate School of Informatics at Strathclyde University. It had radical aims (introducing novel ICT skill content in a progressive manner, encouraging deep learning and self-directed study) and used a weekly survey and a post-module survey to investigate its operation. Skills learnt were compared to skills required during student placement in libraries. Conclusions are drawn as to its success in matching the needs of future library professionals
Machine learning and deep learning
Today, intelligent systems that offer artificial intelligence capabilities
often rely on machine learning. Machine learning describes the capacity of
systems to learn from problem-specific training data to automate the process of
analytical model building and solve associated tasks. Deep learning is a
machine learning concept based on artificial neural networks. For many
applications, deep learning models outperform shallow machine learning models
and traditional data analysis approaches. In this article, we summarize the
fundamentals of machine learning and deep learning to generate a broader
understanding of the methodical underpinning of current intelligent systems. In
particular, we provide a conceptual distinction between relevant terms and
concepts, explain the process of automated analytical model building through
machine learning and deep learning, and discuss the challenges that arise when
implementing such intelligent systems in the field of electronic markets and
networked business. These naturally go beyond technological aspects and
highlight issues in human-machine interaction and artificial intelligence
servitization.Comment: Published online first in Electronic Market
A Survey on Deep Semi-supervised Learning
Deep semi-supervised learning is a fast-growing field with a range of
practical applications. This paper provides a comprehensive survey on both
fundamentals and recent advances in deep semi-supervised learning methods from
model design perspectives and unsupervised loss functions. We first present a
taxonomy for deep semi-supervised learning that categorizes existing methods,
including deep generative methods, consistency regularization methods,
graph-based methods, pseudo-labeling methods, and hybrid methods. Then we offer
a detailed comparison of these methods in terms of the type of losses,
contributions, and architecture differences. In addition to the past few years'
progress, we further discuss some shortcomings of existing methods and provide
some tentative heuristic solutions for solving these open problems.Comment: 24 pages, 6 figure
Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges
Machine Learning algorithms have had a profound impact on the field of
computer science over the past few decades. These algorithms performance is
greatly influenced by the representations that are derived from the data in the
learning process. The representations learned in a successful learning process
should be concise, discrete, meaningful, and able to be applied across a
variety of tasks. A recent effort has been directed toward developing Deep
Learning models, which have proven to be particularly effective at capturing
high-dimensional, non-linear, and multi-modal characteristics. In this work, we
discuss the principles and developments that have been made in the process of
learning representations, and converting them into desirable applications. In
addition, for each framework or model, the key issues and open challenges, as
well as the advantages, are examined
Photonic Neural Networks and Optics-informed Deep Learning Fundamentals
The recent explosive compute growth, mainly fueled by the boost of AI and
DNNs, is currently instigating the demand for a novel computing paradigm that
can overcome the insurmountable barriers imposed by conventional electronic
computing architectures. PNNs implemented on silicon integration platforms
stand out as a promising candidate to endow NN hardware, offering the potential
for energy efficient and ultra-fast computations through the utilization of the
unique primitives of photonics i.e. energy efficiency, THz bandwidth and
low-latency. Thus far, several demonstrations have revealed the huge potential
of PNNs in performing both linear and non-linear NN operations at unparalleled
speed and energy consumption metrics. Transforming this potential into a
tangible reality for DL applications requires, however, a deep understanding of
the basic PNN principles, requirements and challenges across all constituent
architectural, technological and training aspects. In this tutorial, we,
initially, review the principles of DNNs along with their fundamental building
blocks, analyzing also the key mathematical operations needed for their
computation in a photonic hardware. Then, we investigate, through an intuitive
mathematical analysis, the interdependence of bit precision and energy
efficiency in analog photonic circuitry, discussing the opportunities and
challenges of PNNs. Followingly, a performance overview of PNN architectures,
weight technologies and activation functions is presented, summarizing their
impact in speed, scalability and power consumption. Finally, we provide an
holistic overview of the optics-informed NN training framework that
incorporates the physical properties of photonic building blocks into the
training process in order to improve the NN classification accuracy and
effectively elevate neuromorphic photonic hardware into high-performance DL
computational settings
Deep Learning in Medical Image Analysis
The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements
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