Sökning: "Auto Machine Learning Framework"

Visar resultat 1 - 5 av 11 uppsatser innehållade orden Auto Machine Learning Framework.

1. 1. Auto-Tuning Apache Spark Parameters for Processing Large Datasets

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

   Författare :Shidi Zhou; [2023]
   Nyckelord :Apache Spark; Cloud Environment; Spark Configuration Parameter; Resource Utilization; Ridge Regression; Elastic Net; Random Forest; Deep Neural Network; Bayesian Optimization; Particle Swarm Optimization.; Apache Spark; Molnmiljö; Apache Spark konfigurationsparameter; Resursutnyttjande; Ridge-regression; Elastisk nät; Slumpskog; Djupt neuralt nätverk; Bayesiansk optimering; Partikelsvärmsoptimering.;

   Sammanfattning : Apache Spark is a popular open-source distributed processing framework that enables efficient processing of large amounts of data. Apache Spark has a large number of configuration parameters that are strongly related to performance. Selecting an optimal configuration for Apache Spark application deployed in a cloud environment is a complex task. LÄS MER

3. 2. Enhancing Influencer Marketing Strategies through Machine Learning : Predictive Analysis of Influencer-Generated Interactions

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

   Författare :Olimpia Rivera; [2023]
   Nyckelord :Influencer Marketing; Affiliate links; Auto Machine Learning Framework; Predictive analysis; Influencer marketing; Affiliate länkar; maskininlärningsramverk; förutsägbar analys;

   Sammanfattning : The field of influencer marketing has experienced rapid growth in recent years. However, uncovering the true effectiveness of this marketing approach remains a significant challenge. LÄS MER

4. 3. Anomalous Behavior Detection in Aircraft based Automatic Dependent Surveillance–Broadcast (ADS-B) system using Deep Graph Convolution and Generative model (GA-GAN)

   Magister-uppsats, Linköpings universitet/Databas och informationsteknik

   Författare :Jayesh Kenaudekar; [2022]
   Nyckelord :Intrusion detection aircraft aviation security adsb protocol AI deep learning machine learning graph generative model surveillance broadcast;

   Sammanfattning : The Automatic Dependent Surveillance-Broadcast (ADS-B) is a key component of the Next Generation Air Transportation System (Next Gen) that manages the increasingly congested airspace and operation. From Jan 2020, the U.S. Federal Aviation Administration (FAA) mandated the use of (ADS-B) as a key component of Next Gen project. LÄS MER

5. 4. Towards topology-aware Variational Auto-Encoders : from InvMap-VAE to Witness Simplicial VAE

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

   Författare :Aniss Aiman Medbouhi; [2022]
   Nyckelord :Variational Auto-Encoder; Nonlinear dimensionality reduction; Generative model; Inverse projection; Computational topology; Algorithmic topology; Topological Data Analysis; Data visualisation; Unsupervised representation learning; Topological machine learning; Betti number; Simplicial complex; Witness complex; Simplicial map; Simplicial regularization.; Variations autokodare; Ickelinjär dimensionalitetsreducering; Generativ modell; Invers projektion; Beräkningstopologi; Algoritmisk topologi; Topologisk Data Analys; Datavisualisering; Oövervakat representationsinlärning; Topologisk maskininlärning; Betti-nummer; Simplicielt komplex; Vittneskomplex; Simpliciel avbildning; Simpliciel regularisering.;

   Sammanfattning : Variational Auto-Encoders (VAEs) are one of the most famous deep generative models. After showing that standard VAEs may not preserve the topology, that is the shape of the data, between the input and the latent space, we tried to modify them so that the topology is preserved. LÄS MER

7. 5. Data Collection and Layout Analysis on Visually Rich Documents using Multi-Modular Deep Learning.

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

   Författare :Mattias Stahre; [2022]
   Nyckelord :DeepLearning; Machine Learning; Dataset Collection; Annotation; Labeling; Transformer Network; Multi-Modal; Computer Vision; Natural Language Processing; Embedding; LayoutLMv2; DocBank; Djupinlärning; Maskininlärning; Datasamling; Annotering; Märkning; Transformernätverk; Multi-modulär; Datorsyn; Naturlig Språkbehandling; Inbäddning; LayoutLMv2; DocBank;

   Sammanfattning : The use of Deep Learning methods for Document Understanding has been embraced by the research community in recent years. A requirement for Deep Learning methods and especially Transformer Networks, is access to large datasets. LÄS MER