Sökning: "Distribuerad Maskininlärning"

Visar resultat 1 - 5 av 19 uppsatser innehållade orden Distribuerad Maskininlärning.

1. 1. Predicting resource usage on a Kubernetes platform using Machine Learning Methods

   Master-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

   Författare :Arvid Gördén; [2023]
   Nyckelord :Master thesis; Kubernetes; scaling; resource management; horizontal pod autoscaler; vertical scaling; machine learning; Masterarbete; Kubernetes; scaling; resurshantering; horisontell pod autoscaler; vertikal scaling; maskininlärning;

   Sammanfattning : Cloud computing and containerization has been on the rise in recent years and have become important areas of research and development in the field of computer science. One of the challenges in distributed and cloud computing is to predict the resource utilization of the nodes that run the applications and services. LÄS MER

3. 2. LDPC DropConnect

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

   Författare :Xi Chen; [2023]
   Nyckelord :Bayesian approach; Machine learning; Coding theory; Measurement uncertainty; Algorithms; Bayesiansk metod; Maskininlärning; Kodningsteori; Mätosäkerhet; Algoritmer;

   Sammanfattning : Machine learning is a popular topic that has become a scientific research tool in many fields. Overfitting is a common challenge in machine learning, where the model fits the training data too well and performs poorly on new data. LÄS MER

4. 3. Improving Co-existence of URLLC and Distributed AI using RL

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

   Författare :Wei Shi; [2023]
   Nyckelord :5G; URLLC; RL; HRL; Optimization; 5G; URLLC; RL; HRL; Optimering;

   Sammanfattning : In 5G, Ultra-reliable and low-Latency communications (URLLC) service is envisioned to enable use cases with strict reliability and latency requirements on wireless communication. For the upcoming 6G network, machine learning (ML) also stands an important role that introduces intelligence and further enhances the system performance. LÄS MER

5. 4. Federated Learning for Natural Language Processing using Transformers

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

   Författare :Gustav Kjellberg; [2022]
   Nyckelord :Machine Learning; Federated Learning; Distributed Machine Learning; Natural Language Processing; BERT; ALBERT; Transformers; Data Privacy.; Maskininlärning; Federerad inlärning; Distribuerad Maskininlärning; Språkteknologi; BERT; ALBERT; Transformers; Dataintegritet.;

   Sammanfattning : The use of Machine Learning (ML) in business has increased significantly over the past years. Creating high quality and robust models requires a lot of data, which is at times infeasible to obtain. As more people are becoming concerned about their data being misused, data privacy is increasingly strengthened. LÄS MER

7. 5. Evaluating Distributed Machine Learning for Fog Computing loT scenarios : A Comparison Between Distributed and Cloud-based Training on Tensorflow

   Kandidat-uppsats, Mittuniversitetet/Institutionen för informationssystem och –teknologi

   Författare :Hassan El Ghamri; [2022]
   Nyckelord :Machine learning; distributed machine learning; IoT; cloud computing; fog computing; Tensorflow; Maskininlärning; distribuerad maskininlärning; sakernas internet; molntjänster; fog computing; Tensorflow;

   Sammanfattning : Dag för dag blir sakernas internet-enheter (IoT) en större del av vårt liv. För närvarande är dessa enheter starkt beroende av molntjänster vilket kan utgöra en integritetsrisk. Det allmänna syftet med denna rapport är att undersöka alternativ till molntjänster, ett ganska fascinerande alternativ är fog computing. LÄS MER