Explainable Deep Neural Networks Research Topic Ideas for PhD
Recent Advances in Deep Recurrent Neural Networks
Deep Neural Network architecture
Examples of deep neural networks. a Deep feedforward neural network
A Friendly Introduction to [Deep] Neural Networks
Deep Neural Networks as Scientific Models: Trends in Cognitive Sciences
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How Deep Neural Networks Work
But what is a neural network?
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How Deep Neural Networks Work
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deep learning Latest Research Papers
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 neural networks (DCNNs) and involves a mutual learning process from one another.
Deep learning: systematic review, models, challenges, and research ...
In , the authors highlighted different DL-based models, such as deep neural networks, convolutional neural networks, recurrent neural networks, and auto-encoders. They …
Deep Neural Network
Deep neural networks are a type of artificial neural network with multiple hidden layers, which makes them more complex and resource-intensive compared to conventional neural networks. …
Review of deep learning: concepts, CNN architectures, …
It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet …
Current progress and open challenges for applying deep ...
In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome …
If deep learning is the answer, what is the question?
Many researchers are excited by the possibility that deep neural networks may offer theories of perception, cognition and action for biological brains.
Combining deep neural network and bibliometric indicator for …
Deep neural networks, specifically LSTM and NNAR, are applied with nine features of topics to predict popularity score. We evaluated the models and five baselines on …
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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 neural networks (DCNNs) and involves a mutual learning process from one another.
In , the authors highlighted different DL-based models, such as deep neural networks, convolutional neural networks, recurrent neural networks, and auto-encoders. They …
Deep neural networks are a type of artificial neural network with multiple hidden layers, which makes them more complex and resource-intensive compared to conventional neural networks. …
It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet …
In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome …
Many researchers are excited by the possibility that deep neural networks may offer theories of perception, cognition and action for biological brains.
Deep neural networks, specifically LSTM and NNAR, are applied with nine features of topics to predict popularity score. We evaluated the models and five baselines on …