Sökning: "Long Run Memory"

Visar resultat 1 - 5 av 30 uppsatser innehållade orden Long Run Memory.

  1. 1. Optimizing Quantum Computer Simulations With Data Compression & GPU Acceleration

    Kandidat-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

    Författare :Erik Ljung; Darko Petrov; Felix Bråberg; Björn Forssén; Beata Ringmar; Jonas Hedlund; [2023-03-03]
    Nyckelord :;

    Sammanfattning : Simulating quantum computers involves high memory usage and often long execution times. For that reason the purpose of this project was to analyze whether data compression and GPU acceleration can be used to run simulations with more qubits than previously allowed. LÄS MER

  3. 2. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition

    Master-uppsats, KTH/Mekatronik och inbyggda styrsystem

    Författare :Helgi Hrafn Björnsson; Jón Kaldal; [2023]
    Nyckelord :Recurrent Neural Networks; Long Short-Term Memory Networks; Embedded Systems; Human Activity Recognition; Edge AI; TensorFlow Lite Micro; Recurrent Neural Networks; Long Short-Term Memory Networks; Innbyggda systyem; Mänsklig aktivitetsigenkänning; Edge AI; TensorFlow Lite Micro;

    Sammanfattning : 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. LÄS MER

  4. 3. Deep Learning in the Web Browser for Wind Speed Forecasting using TensorFlow.js

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Sara Moazez Gharebagh; [2023]
    Nyckelord :TensorFlow.js; JavaScript; Artificial Neural Networks; Deep Learning; Recurrent Neural Networks; Long Short-Term Memory; GatedRecurrent Units; TensorFlow.js; JavaScript; Artificiella Neurala Nätverk; Djupinlärning; Recurrent Neural Networks; Long Short-Term Memory; GatedRecurrent Units;

    Sammanfattning : 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. LÄS MER

  5. 4. Passenger flow prediction : Finding and developing a sustainable machine learning model for airport passenger flow prediction

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Matematiska institutionen

    Författare :Tomas Haglund; Oskar Jonsson; [2023]
    Nyckelord :Maskininlärning flyplats flyg flygindustrin passanger flow AI;

    Sammanfattning : There are many outdated routines and processes in today's aviation industry that major airlines lack the motivation to update. While this may not hold any direct security concerns, it creates bottlenecks at checks and high salary costs for otiose airport personnel. LÄS MER

  7. 5. Low Power Hardware Accelerator For Gated Recurrent Unit

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Malavika Balakumar; [2022]
    Nyckelord :;

    Sammanfattning : Neural Networks are a subset of Machine Learning which are designed to recognize patterns. Recurrent Neural Networks are an important part of AI (Artificial Intelligence) which allows for short term as well as long term dependencies to be captured. LÄS MER