Essays about: "Long Short-Term Memory Recurrent Neural Networks LSTM"
Showing result 1 - 5 of 68 essays containing the words Long Short-Term Memory Recurrent Neural Networks LSTM.
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1. Drivers of sea level variability using neural networks
University essay from Göteborgs universitet/Institutionen för geovetenskaperAbstract : Understanding the forcing of regional sea level variability is crucial as many people all over the world live along the coasts and are endangered by extreme sea levels and the global sea level rise. The adding of fresh water into the oceans due to melting of the Earth’s land ice together with thermosteric changes has led to a rise of the global mean sea level with an accelerating rate during the twentieth century. READ MORE
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2. Modelling Proxy Credit Cruves Using Recurrent Neural Networks
University essay from KTH/Matematisk statistikAbstract : Since the global financial crisis of 2008, regulatory bodies worldwide have implementedincreasingly stringent requirements for measuring and pricing default risk in financialderivatives. Counterparty Credit Risk (CCR) serves as the measure for default risk infinancial derivatives, and Credit Valuation Adjustment (CVA) is the pricing method used toincorporate this default risk into derivatives prices. READ MORE
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3. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition
University essay from KTH/Mekatronik och inbyggda styrsystemAbstract : Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. READ MORE
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4. Unauthorised Session Detection with RNN-LSTM Models and Topological Data Analysis
University essay from KTH/Matematik (Avd.)Abstract : This thesis explores the possibility of using session-based customers data from Svenska Handelsbanken AB to detect fraudulent sessions. Tools within Topological Data Analysis are employed to analyse customers behavior and examine topological properties such as homology and stable rank at the individual level. READ MORE
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5. Artificial Neural Networks for Financial Time Series Prediction
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : Financial market forecasting is a challenging and complex task due to the sensitivity of the market to various factors such as political, economic, and social factors. However, recent advances in machine learning and computation technology have led to an increased interest in using deep learning for forecasting financial data. READ MORE
