Deep Learning and Social Networks Use Cases
Deep Learning based Trust Aware Recommender for Social Networks
... B. Proposed System Overview This paper mainly focuses on the Trust-Based recommendations; Memory-based approaches have largely figured on integrating trust into recommendations. The most common RSs cause users to issue ...
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Incremental Learning in Deep Neural Networks
... incremental learning framework based on deep neural networks to improve both performance and efficiency ...incremental learning framework is in a manner of ...to use the networks ...
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Social Networks in University Students: Academic Use and Learning Scenarios
... the use of this technology, the following is suggested: 1) the teacher should know how to manage different social networking tools, to properly configure their profile to interact with their students ...
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An Overview of Machine Learning, Deep Learning and Neural Networks
... machine learning is that these algorithms are used as classifiers to give labels to each set of data and further use mathematics ( probability and statistics ) to assign equations and calculate error rate , ...
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A Deep Hybrid Graph Kernel through Deep Learning Networks
... We use the embedding vectors of all graphs to train a deep autoencoder network, that is optimized using Stochastic Gradient Descent together with a Deep Belief Network for ...the deep ...
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Efficient use of deep learning and machine learning for load forecasting in South African power distribution networks
... Machine learning has been widely used across multiple fields such as engineering, power systems, medicine, economics, insurance, gaming, social media, law, emergency response, search and rescue, online ...
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Deep Machine Learning and Neural Networks: An Overview
... Active Learning The preceding overview of generative and discriminative ML paradigms uses the attributes of loss and decision functions to organize a multitude of ML ...we use a different set of attributes, ...
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Reinforcement Learning with Deep Quantum Neural Networks
... of learning from experience, RL is a method of solving sequential decision-making problems with an agent by trial and error in a known (with a model) or unknown (without a model) environ- ...can use to ...
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Neural Networks and Deep Learning - Michael Nielsen
... understand when and why rectified linear units perform better than sigmoid or tanh neurons. I've painted a picture of uncertainty here, stressing that we do not yet have a solid theory of how activation functions should ...
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Distributed deep learning inference in fog networks
... This section describes a distributed algorithm for offloading the DNN task to preferable fog nodes based on computation time, communication latency, and queuing time. After the partition, the sub-tasks are offloaded to ...
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Deep learning neural networks in malaria diagnosis
... These functions are added at the output of each convolutional layer and also after fully connected layers. Their goal is to provide a non-linear activation of the outputs of a convolution and general non-linearity to the ...
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Superintelligent Deep Learning Artificial Neural Networks
... These neuronal functions are reflected in the anatomy of the neuron. Humans have a hundred billion (100, 000, 000, 000) neurons in our body system, most of those centered in the central nervous system in the brain and ...
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Deep learning Transformer Networks. François Fleuret
... We use Adam with learning rate of 1e-4, β 1 = ...0.01, learning rate warmup over the first 10,000 steps, and linear decay of the learning ...We use a dropout prob- ability of 0.1 on all ...
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Learning Activation Functions in Deep Neural Networks
... of deep neural networks (deep learning) achieved considerable success in pattern recognition and text ...of deep learning on images, video or text classification, the application ...
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Deep depth-based representations of graphs through deep learning networks
... machine learning. In this paper, we propose a framework of computing the deep depth-based representations for graph ...and deep learning ...to use the prototype representations to train ...
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Analyzing and inferring human real-life behavior through online social networks with social influence deep learning
... The trade-off is solved by C-SIDL, which achieves performance close to the G-SIDL with limited issues in scalability: a new user in the OSN requires only to retrain one C- SIDL, whose computational time is about 10 times ...
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Social Networks as a Learning and Teaching Environment and Security in Social Networks
... widespread use of the internet over time, and shortly thereafter examples of use in education began to ...reading, social networks that make it possible to comment, create content, ...
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Essays on Social Learning and Networks
... sequential social learning introduced separately by Bihchandani, Hirshleifer, Welch (1992) and Banerjee ...both cases. Finally, we compare the learning speed to the one that we obtain when ...
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Learning and status in social networks
... to use time as a screening device so that when an individual makes a decision, the timing of the announcement, in addition to the content, transmits useful information to ...
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Social Networks for Language Learning
... their learning by being up-to-date and self- ...can use e-mail to communicate with their teachers and with second language speakers or native ...English learning. The review would conclude that ...
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