Deep Learning / hybrid

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Hybrid Malicious Code Detection using Deep Learning with Keras and Scikit Learn

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A Hybrid recommendation engine built on deep learning architecture, which has the potential to combine content-based and collaborative filtering recommendation mechanisms using a deep learning supervisor

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Common code for building neural networks with a hybrid of Pytorch and Tensorflow eager execution

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Implementation of the hybrid deep-learning steganalytic framework described in http://arxiv.org/abs/1611.03233

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Deep Learning Models for Hybrid Brain Computer Interfaces

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HybridTox2D πŸ‚
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In recent times, toxicological classification of chemical compounds is considered to be a grand challenge for pharma-ceutical and environment regulators. Advancement in machine learning techniques enabled efficient toxicity predic-tion pipelines. Random forests (RF), support vector machines (SVM) and deep neural networks (DNN) are often ap-plied to model the toxic effects of chemical compounds. However, complexity-accuracy tradeoff still needs to be ac-counted in order to improve the efficiency and commercial deployment of these methods. In this study, we implement a hybrid framework consists of a shallow neural network and a decision classifier for toxicity prediction of chemicals that interrupt nuclear receptor (NR) and stress response (SR) signaling pathways. A model based on proposed hybrid framework is trained on Tox21 data using 2D chemical descriptors that are less multifarious in nature and easy to calcu-late. Our method achieved the highest accuracy of 0.847 AUC (area under the curve) using a shallow neural network with only one hidden layer consisted of 10 neurons. Furthermore, our hybrid model enabled us to elucidate the inter-pretation of most important descriptors responsible for NR and SR toxicity.

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DeepNovo 🌿
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Protein Identification with Deep Learning

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deep learning for recommender system

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(no description)

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Source code for paper Deep Learning for Direct Hybrid Precoding in Millimeter Wave Massive MIMO Systems

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