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2020
Deep learning (DL) is a family of machine learning methods capable of detecting multiple levels of latent representations from the data. This is achieved by combining consecutive layers of simple nonlinear transformations that allow the extraction of increasingly abstract features.
Vieira, Sandra +3 more
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Deep learning (DL) is a family of machine learning methods capable of detecting multiple levels of latent representations from the data. This is achieved by combining consecutive layers of simple nonlinear transformations that allow the extraction of increasingly abstract features.
Vieira, Sandra +3 more
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2019
In Chap. 1, our empirical analysis was based on neural networks with a single hidden layer. These networks, called shallow, are in theory universal approximators of any continuous function. Deep neural networks use instead a cascade of multiple layers of hidden neurons. Each successive layer uses the output from the previous layer as input.
Michel Denuit +2 more
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In Chap. 1, our empirical analysis was based on neural networks with a single hidden layer. These networks, called shallow, are in theory universal approximators of any continuous function. Deep neural networks use instead a cascade of multiple layers of hidden neurons. Each successive layer uses the output from the previous layer as input.
Michel Denuit +2 more
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2019
We will implement a multi-layered neural network with different hyperparameters Hidden layer activations Hidden layer nodes Output layer activation Learning rate Mini-batch size Initialization Value of \(\beta \) Values of \(\beta _1\) Value of \(\beta _2\) Value of \(\epsilon \) Value of keep_prob
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We will implement a multi-layered neural network with different hyperparameters Hidden layer activations Hidden layer nodes Output layer activation Learning rate Mini-batch size Initialization Value of \(\beta \) Values of \(\beta _1\) Value of \(\beta _2\) Value of \(\epsilon \) Value of keep_prob
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2021
Quantitative structure-activity relationship (QSAR) models are routinely applied computational tools in the drug discovery process. QSAR models are regression or classification models that predict the biological activities of molecules based on the features derived from their molecular structures.
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Quantitative structure-activity relationship (QSAR) models are routinely applied computational tools in the drug discovery process. QSAR models are regression or classification models that predict the biological activities of molecules based on the features derived from their molecular structures.
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2014
In this chapter, we introduce deep neural networks (DNNs)—multilayer perceptrons with many hidden layers. DNNs play an important role in the modern speech recognition systems, and are the focus of the rest of the book. We depict the architecture of DNNs, describe the popular activation functions and training criteria, illustrate the famous ...
Dong Yu, Li Deng
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In this chapter, we introduce deep neural networks (DNNs)—multilayer perceptrons with many hidden layers. DNNs play an important role in the modern speech recognition systems, and are the focus of the rest of the book. We depict the architecture of DNNs, describe the popular activation functions and training criteria, illustrate the famous ...
Dong Yu, Li Deng
openaire +1 more source
Manifold regularized deep neural networks
Interspeech 2014, 2014Deep neural networks (DNNs) have been successfully applied to a variety of automatic speech recognition (ASR) tasks, both in discriminative feature extraction and hybrid acoustic modeling scenarios. The development of improved loss functions and regularization approaches have resulted in consistent reductions in ASR word error rates (WERs).
Vikrant Singh Tomar, Rose, Richard C.
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Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR
Nature Reviews Drug Discovery, 2023Artem Cherkasov +2 more
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Deep Learning for Geophysics: Current and Future Trends
Reviews of Geophysics, 2021Siwei Yu, Jianwei
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Soft wearable devices for deep-tissue sensing
Nature Reviews Materials, 2022Muyang Lin, Hongjie Hu, Sai Zhou
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