Essays about: "training quality"

Showing result 1 - 5 of 516 essays containing the words training quality.

  1. 1. Variational AutoEncoders and Differential Privacy : balancing data synthesis and privacy constraints

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Baptiste Bremond; [2024]
    Keywords : TVAE; Differential privacy; Tabular data; Synthetic data; DP-SGD; TVAE; differentiell integritet; tabelldata; syntetiska data; DP-SGD;

    Abstract : This thesis investigates the effectiveness of Tabular Variational Auto Encoders (TVAEs) in generating high-quality synthetic tabular data and assesses their compliance with differential privacy principles. The study shows that while TVAEs are better than VAEs at generating synthetic data that faithfully reproduces the distribution of real data as measured by the Synthetic Data Vault (SDV) metrics, the latter does not guarantee that the synthetic data is up to the task in practical industrial applications. READ MORE

  2. 2. Blood flow restriction training for people with chronic obstructive pulmonary disease or heart failure; A scoping review

    University essay from Umeå universitet/Avdelningen för fysioterapi

    Author : Ivar Ramström; Kevin Ulman; [2024]
    Keywords : Cardiac rehabilitation; Pulmonary rehabilitation; KAATSU; BFR; BFRT; Vascular occlusion; HF; COPD;

    Abstract : Background: Blood flow restriction training (BFRT) is an effective way of training that enables training with low external load while receiving similar effects to high load training. The lack of knowledge of BFRT for people with chronic obstructive pulmonary disease (COPD) or heart failure (HF) led to the making of this scoping review. READ MORE

  3. 3. Measuring the Utility of Synthetic Data : An Empirical Evaluation of Population Fidelity Measures as Indicators of Synthetic Data Utility in Classification Tasks

    University essay from Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

    Author : Alexander Florean; [2024]
    Keywords : Synthetic Data; Machine Learning; Population Fidelity Measures; Utility Metrics; Synthetic Data Quality Evaluation; Classification Algorithms; Utility Estimation; Data Privacy; Artificial Intelligence; Experiment Framework; Model Performance Assessment; Syntetisk Data; Maskininlärning; Population Fidelity Mätvärden; Användbarhetsmätvärden; Kvalitetsutvärdering av Syntetisk Data; Klassificeringsalgoritmer; Användbarhetsutvärdering; Dataintegritet; Artificiell Intelligens; AI; Experiment Ramverk; Utvärdering av Modellprestanda;

    Abstract : In the era of data-driven decision-making and innovation, synthetic data serves as a promising tool that bridges the need for vast datasets in machine learning (ML) and the imperative necessity of data privacy. By simulating real-world data while preserving privacy, synthetic data generators have become more prevalent instruments in AI and ML development. READ MORE

  4. 4. Implementing End-to-End MLOps for Enhanced Steel Production

    University essay from Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

    Author : Marcus Westin; Jacob Berggren; [2024]
    Keywords : MLOps; Azure ML; Machine Learning; Computer Science; Microsoft Azure; MLOps; Azure ML; Maskininlärning; Datavetenskap; Microsoft Azure;

    Abstract : Steel production companies must utilize new technologies and innovations to stay ahead of a highly competitive market. Recently, there has been a focus on Industry 4.0, which involves the digitalization of production to integrate with newer technologies such as cloud solutions and the Internet of Things (IoT). READ MORE

  5. 5. Data Augmentation for Object Detection using Deep Reinforcement Learning

    University essay from Lunds universitet/Institutionen för reglerteknik

    Author : Axel Andersson; Nils Hallerfelt; [2024]
    Keywords : Technology and Engineering;

    Abstract : Data augmentation is a concept which is used to improve machine learning models for computer vision tasks. It is usually done by firstly, defining a set of functions which transforms images and secondly, applying a random selection of these functions on the images. READ MORE