(PDF) Deep Learning: An overview and its practical examples
(PDF) Deep Learning
(PDF) A guide to deep learning in healthcare
[View 21+] Image Classification Using Deep Learning Research Papers
(PDF) Review of Deep Learning Algorithms and Architectures
(PDF) Deep Learning is the Core Method of Machine Learning
VIDEO
Why you should read Research Papers in ML & DL? #machinelearning #deeplearning
Implementing a Deep Learning Research paper in python (Part -1)
Deep Learning Overview
Deep Learning Facts 116#shorts
Deep Learning Facts 133#shorts
Deep Learning Facts 148#shorts
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(PDF) Deep Learning
A deep-learning architecture is a mul tilayer stack of simple mod- ules, all (or most) of which are subject to learning, and man y of which compute non-linea r input-outpu t mappings.
The Principles of Deep Learning Theory arXiv:2106.10165v2 [cs.LG] 24
This type of expansion is known as the1/nexpansionorlarge-nexpansionand will be one of our main tools for learning the principles of deep learning theory. Aside: statistical independence and interactions. The quartic action (1.71) is one of the simplest models of aninteracting theory.
(PDF) DEEP LEARNING: A REVIEW
PDF | Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. ... method was adopted to select papers for review ...
PDF Deep Learning: A Comprehensive Overview on Techniques ...
This paper is organized as follows. Section "Why Deep Learning in Today's Research andApplications?" motivates why deep learning is important to build data-driven intel-ligent systems. In Section" Deep Learning Techniques and Applications", we present our DL taxonomy by taking into account the variations of deep learning tasks and how they
[1404.7828] Deep Learning in Neural Networks: An Overview
View PDF Abstract: In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable ...
Deep Learning: A Comprehensive Overview on Techniques ...
Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various ...
Deep Learning as a Frontier of Machine Learning: A Review
Download full-text PDF Read full-text. Download full-text PDF. ... Discover the world's research. 25+ million members; ... Deep Learning (DL) is a subset of machine learning (ML) which is an ...
PDF Review of deep learning: concepts, CNN architectures, challenges
time and a large number of research papers to learn about DL including research gaps and applications. erefore, we propose a deep review of DL to provide a more suit- ... approaches. e most keywords used for search criteria for this review paper are ("Deep Learning"), ("Machine Learning"), ("Convolution Neural Network"), ("Deep ...
PDF Deep learning: emerging trends, applications and research ...
research and development achievements in exploring techniques, applications, and challenges that face the evolution of artificial intelligence in the context deep learning. A brief overview of the papers is presented and discussed as follows: The first theme in this special issue focuses on ''Re-duction of parameters in deep-learning ...
This Paper Has Been Accepted by Ieee Transactions on Neural Networks
Prior to overview on deep learning based object detection approaches, we provide a review on the history of deep learning along with an introduction on the basic architecture and advantages of CNN. A. The History: Birth, Decline and Prosperity Deep models can be referred to as neural networks with deep structures.
A Survey of Deep Learning: Platforms, Applications and Emerging
Deep learning has exploded in the public consciousness, primarily as predictive and analytical products suffuse our world, in the form of numerous human-centered smart-world systems, including targeted advertisements, natural language assistants and interpreters, and prototype self-driving vehicle systems. Yet to most, the underlying mechanisms that enable such human-centered smart products ...
Review of deep learning: concepts, CNN architectures, challenges
Recently, machine learning (ML) has become very widespread in research and has been incorporated in a variety of applications, including text mining, spam detection, video recommendation, image classification, and multimedia concept retrieval [1,2,3,4,5,6].Among the different ML algorithms, deep learning (DL) is very commonly employed in these applications [7,8,9].
70 recent research papers in Deep Learning
In this post, we list the top 70 research papers and projects in deep learning, published recently. Feel free to download. Share your own research papers with us to be added to this list. Person re-identification by deep learning multi-scale representations. Classification of Diabetic Retinopathy Images by Using Deep Learning Models.
deep learning Latest Research Papers
The application of recent artificial intelligence (AI) and deep learning (DL) approaches integrated to radiological images finds useful to accurately detect the disease. This article introduces a new synergic deep learning (SDL)-based smart health diagnosis of COVID-19 using Chest X-Ray Images. The SDL makes use of dual deep convolutional ...
The application of deep learning in computer vision
As the deep learning exhibits strong advantages in the feature extraction, it has been widely used in the field of computer vision and among others, and gradually replaced traditional machine learning algorithms. This paper first reviews the main ideas of deep learning, and displays several related frequently-used algorithms for computer vision. Afterwards, the current research status of ...
Machine Learning
uses-cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real-world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are
(PDF) Deep Learning Techniques: An Overview
Download full-text PDF ... It presents a subway station area crossing detection algorithm based on the YOLOv5s deep learning algorithm. In this paper, the network structure of the YOLOv5s ...
Computers
In this systematic literature review, the intersection of deep learning applications within the aphasia domain is meticulously explored, acknowledging the condition's complex nature and the nuanced challenges it presents for language comprehension and expression. By harnessing data from primary databases and employing advanced query methodologies, this study synthesizes findings from 28 ...
PDF ImageNet Classification with Deep Convolutional Neural Networks
[12]A. Krizhevsky. Learning multiple layers of features from tiny images. Master's thesis, Department of Computer Science, University of Toronto, 2009. [13]A. Krizhevsky. Convolutional deep belief networks on cifar-10. Unpublished manuscript, 2010. [14]A. Krizhevsky and G.E. Hinton. Using very deep autoencoders for content-based image ...
(PDF) Deep learning review and discussion of its future development
In this paper, from the perspective of bibliometrics, a comprehensive analysis of publications of DL is deployed from 2007 to 2019 (the first publication with keywords "deep learning" and ...
[2104.05314] 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 ...
Deepfake Detection: A Systematic Literature Review
Over the last few decades, rapid progress in AI, machine learning, and deep learning has resulted in new techniques and various tools for manipulating multimedia. Though the technology has been mostly used in legitimate applications such as for entertainment and education, etc., malicious users have also exploited them for unlawful or nefarious purposes. For example, high-quality and realistic ...
Deep learning in drug discovery: an integrative review and future
Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that ...
(PDF) Deep Learning and Its Applications: A Review
Download full-text PDF ... paper presents a review on deep learning and its applications over the years, with a goal of providing useful references to other researchers to get the idea for new ...
Employers
IT Professional Internship Information for Employers Procedure Every year invitations to join the scheme are sent to companies around November. The internship period is 9 months and it starts in summer, earliest in July and latest in September. Students work in internship 4 days/week, with Fridays off to attend classes at CityU. Employers will interview students prior to confirmation of ...
Deep Learning for Deepfakes Creation and Detection: A Survey
the number of deepfake papers has increased signifi-cantly in recent years (Fig. 1). Although the obtained numbers of deepfake papers may be lower than actual numbers but the research trend of this topic is obviously increasing. There have been existing survey papers about creat-ing and detecting deepfakes, presented in [19, 20, 32].
IMAGES
VIDEO
COMMENTS
A deep-learning architecture is a mul tilayer stack of simple mod- ules, all (or most) of which are subject to learning, and man y of which compute non-linea r input-outpu t mappings.
This type of expansion is known as the1/nexpansionorlarge-nexpansionand will be one of our main tools for learning the principles of deep learning theory. Aside: statistical independence and interactions. The quartic action (1.71) is one of the simplest models of aninteracting theory.
PDF | Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. ... method was adopted to select papers for review ...
This paper is organized as follows. Section "Why Deep Learning in Today's Research andApplications?" motivates why deep learning is important to build data-driven intel-ligent systems. In Section" Deep Learning Techniques and Applications", we present our DL taxonomy by taking into account the variations of deep learning tasks and how they
View PDF Abstract: In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable ...
Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various ...
Download full-text PDF Read full-text. Download full-text PDF. ... Discover the world's research. 25+ million members; ... Deep Learning (DL) is a subset of machine learning (ML) which is an ...
time and a large number of research papers to learn about DL including research gaps and applications. erefore, we propose a deep review of DL to provide a more suit- ... approaches. e most keywords used for search criteria for this review paper are ("Deep Learning"), ("Machine Learning"), ("Convolution Neural Network"), ("Deep ...
research and development achievements in exploring techniques, applications, and challenges that face the evolution of artificial intelligence in the context deep learning. A brief overview of the papers is presented and discussed as follows: The first theme in this special issue focuses on ''Re-duction of parameters in deep-learning ...
Prior to overview on deep learning based object detection approaches, we provide a review on the history of deep learning along with an introduction on the basic architecture and advantages of CNN. A. The History: Birth, Decline and Prosperity Deep models can be referred to as neural networks with deep structures.
Deep learning has exploded in the public consciousness, primarily as predictive and analytical products suffuse our world, in the form of numerous human-centered smart-world systems, including targeted advertisements, natural language assistants and interpreters, and prototype self-driving vehicle systems. Yet to most, the underlying mechanisms that enable such human-centered smart products ...
Recently, machine learning (ML) has become very widespread in research and has been incorporated in a variety of applications, including text mining, spam detection, video recommendation, image classification, and multimedia concept retrieval [1,2,3,4,5,6].Among the different ML algorithms, deep learning (DL) is very commonly employed in these applications [7,8,9].
In this post, we list the top 70 research papers and projects in deep learning, published recently. Feel free to download. Share your own research papers with us to be added to this list. Person re-identification by deep learning multi-scale representations. Classification of Diabetic Retinopathy Images by Using Deep Learning Models.
The application of recent artificial intelligence (AI) and deep learning (DL) approaches integrated to radiological images finds useful to accurately detect the disease. This article introduces a new synergic deep learning (SDL)-based smart health diagnosis of COVID-19 using Chest X-Ray Images. The SDL makes use of dual deep convolutional ...
As the deep learning exhibits strong advantages in the feature extraction, it has been widely used in the field of computer vision and among others, and gradually replaced traditional machine learning algorithms. This paper first reviews the main ideas of deep learning, and displays several related frequently-used algorithms for computer vision. Afterwards, the current research status of ...
uses-cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real-world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are
Download full-text PDF ... It presents a subway station area crossing detection algorithm based on the YOLOv5s deep learning algorithm. In this paper, the network structure of the YOLOv5s ...
In this systematic literature review, the intersection of deep learning applications within the aphasia domain is meticulously explored, acknowledging the condition's complex nature and the nuanced challenges it presents for language comprehension and expression. By harnessing data from primary databases and employing advanced query methodologies, this study synthesizes findings from 28 ...
[12]A. Krizhevsky. Learning multiple layers of features from tiny images. Master's thesis, Department of Computer Science, University of Toronto, 2009. [13]A. Krizhevsky. Convolutional deep belief networks on cifar-10. Unpublished manuscript, 2010. [14]A. Krizhevsky and G.E. Hinton. Using very deep autoencoders for content-based image ...
In this paper, from the perspective of bibliometrics, a comprehensive analysis of publications of DL is deployed from 2007 to 2019 (the first publication with keywords "deep learning" and ...
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 ...
Over the last few decades, rapid progress in AI, machine learning, and deep learning has resulted in new techniques and various tools for manipulating multimedia. Though the technology has been mostly used in legitimate applications such as for entertainment and education, etc., malicious users have also exploited them for unlawful or nefarious purposes. For example, high-quality and realistic ...
Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that ...
Download full-text PDF ... paper presents a review on deep learning and its applications over the years, with a goal of providing useful references to other researchers to get the idea for new ...
IT Professional Internship Information for Employers Procedure Every year invitations to join the scheme are sent to companies around November. The internship period is 9 months and it starts in summer, earliest in July and latest in September. Students work in internship 4 days/week, with Fridays off to attend classes at CityU. Employers will interview students prior to confirmation of ...
the number of deepfake papers has increased signifi-cantly in recent years (Fig. 1). Although the obtained numbers of deepfake papers may be lower than actual numbers but the research trend of this topic is obviously increasing. There have been existing survey papers about creat-ing and detecting deepfakes, presented in [19, 20, 32].