Essays about: "Gated Recurrent Units"
Showing result 1 - 5 of 16 essays containing the words Gated Recurrent Units.
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1. Data Driven Model Identification for Remote Electrical Tilt Systems
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : This thesis explores the use of supervised machine learning for modelling the dynamics of Remote Electrical Tilt (RET) telecom systems. Three methodologies, including linear regressionfor linear dynamics models, Gaussian Process (GP) regression, and Recurrent Neural Networks (RNN) with Gated Recurrent Units (GRU) are proposed. READ MORE
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2. Temporal Localization of Representations in Recurrent Neural Networks
University essay from Högskolan Dalarna/Institutionen för information och teknikAbstract : Recurrent Neural Networks (RNNs) are pivotal in deep learning for time series prediction, but they suffer from 'exploding values' and 'gradient decay,' particularly when learning temporally distant interactions. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) have addressed these issues to an extent, but the precise mitigating mechanisms remain unclear. READ MORE
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3. Deep Learning in the Web Browser for Wind Speed Forecasting using TensorFlow.js
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Deep Learning is a powerful and rapidly advancing technology that has shown promising results within the field of weather forecasting. Implementing and using deep learning models can however be challenging due to their complexity. READ MORE
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4. Predicting Waveforms with Machine Learning for Efficient Triggering in Monitoring Systems
University essay from Mälardalens universitet/Akademin för innovation, design och teknikAbstract : Energy systems need to evolve to meet the requirements of the modern world and the future. Hence, substantial effort is needed at an academic and industrial level to develop valuable diagnostic techniques. READ MORE
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5. Graph Neural Network for Traffic Flow Forecasting : Does an enriched adjacency matrix with low dimensional dataenhance the performance of GNN for traffic flow forecasting?
University essay from Högskolan i Halmstad/Akademin för informationsteknologiAbstract : Nowadays, machine learning methods are used in many applications and deployed in manyelectronic devices to solve problems and predict future states. One of the challenges mostbig cities confront is traffic jams since the roads are crammed with more and more vehicles, which will easily cause traffic congestion. READ MORE