Sökning: "Distribuerad maskininlärning"

Visar resultat 6 - 10 av 19 uppsatser innehållade orden Distribuerad maskininlärning.

1. 6. Comparison of Machine Learning Models Used for Swedish Text Classification in Chat Messaging

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

   Författare :Mezbahul Karim; Amirtaha Amanzadi; [2022]
   Nyckelord :Machine learning; Natural language processing NLP ; Text classification; Model deployment; BERT; Maskininlärning; Naturlig språkbehandling NLP ; Textklassificering; Modellinstallation; BERT;

   Sammanfattning : The rise of social media and the use of mobile applications has led to increasing concerns regarding the content that is shared through these apps and whether they are being regulated or not. One of the problems that can arise due to a lack of regulation is that chat messages that are inappropriate or of profane nature can be allowed to be shared through these apps. LÄS MER

3. 7. Cluster selection for Clustered Federated Learning using Min-wise Independent Permutations and Word Embeddings

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

   Författare :Pulasthi Raveen Bandara Harasgama; [2022]
   Nyckelord :Federated learning; Distributed machine learning; Clustering; Word Embeddings; Federerad inlärning; Distribuerad maskininlärning; Klustring; Ordinbäddningar;

   Sammanfattning : Federated learning is a widely established modern machine learning methodology where training is done directly on the client device with local client data and the local training results are shared to compute a global model. Federated learning emerged as a result of data ownership and the privacy concerns of traditional machine learning methodologies where data is collected and trained at a central location. LÄS MER

4. 8. Machine Learning with Reconfigurable Privacy on Resource-Limited Edge Computing Devices

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

   Författare :Zannatun Nayem Tania; [2021]
   Nyckelord :Data Privacy; Resource Management; Machine Learning; Fitbit; Internet of Things IoT ; Optimization; Dataintegritet; Resurshantering; Machine Learning; Fitbit; Internet of Things IoT ; Optimering;

   Sammanfattning : Distributed computing allows effective data storage, processing and retrieval but it poses security and privacy issues. Sensors are the cornerstone of the IoT-based pipelines, since they constantly capture data until it can be analyzed at the central cloud resources. However, these sensor nodes are often constrained by limited resources. LÄS MER

5. 9. Scalable Architecture for Automating Machine Learning Model Monitoring

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

   Författare :Javier de la Rúa Martínez; [2020]
   Nyckelord :Model Monitoring; Streaming; Scalability; Cloud-native; Data Drift; Outliers; Machine Learning; Modellövervakning; Streaming-metod; Skalbarhet; Molnbaserad; Dataskift; Outlierupptäckt; Maskininlärning;

   Sammanfattning : Last years, due to the advent of more sophisticated tools for exploratory data analysis, data management, Machine Learning (ML) model training and model serving into production, the concept of MLOps has gained more popularity. As an effort to bring DevOps processes to the ML lifecycle, MLOps aims at more automation in the execution of diverse and repetitive tasks along the cycle and at smoother interoperability between teams and tools involved. LÄS MER

7. 10. Decentralized Large-Scale Natural Language Processing Using Gossip Learning

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

   Författare :Abdul Aziz Alkathiri; [2020]
   Nyckelord :gossip learning; decentralized machine learning; distributed machine learning; NLP; Word2Vec; data privacy; skvallerinlärning; decentraliserad maskininlärning; distribuerad maskininlärning; naturlig språkbehandling; Word2Vec; dataintegritet;

   Sammanfattning : The field of Natural Language Processing in machine learning has seen rising popularity and use in recent years. The nature of Natural Language Processing, which deals with natural human language and computers, has led to the research and development of many algorithms that produce word embeddings. LÄS MER