Sökning: "Supervised Deep Learning"

Visar resultat 1 - 5 av 165 uppsatser innehållade orden Supervised Deep Learning.

  1. 1. ML implementation for analyzing and estimating product prices

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

    Författare :Abel Getachew Kenea; Gabriel Fagerslett; [2024]
    Nyckelord :Machine Learning; ML; Regression; Deep Learning; Artificial Neural Network; ANN; TensorFlow; ScikitLearn; CUDA; cuDNN; Estimation; Prediction; AI; Artificial Intelligence; Price Tracking; Price Logging; Price Estimation; Supervised Learning; Random Forest; Decision Trees; Batch Learning; Hyperparameter Tuning; Linear Regression; Multiple Linear Regression; Maskininlärning; Djup lärning; Artificiellt Neuralt Nätverk; Regression; TensorFlow; SciktLearn; ML; ANN; Estimation; Uppskattning; CUDA; cuDNN; AI; Artificiell Intelligens; pris loggning; pris estimation; prisspårning; Batchinlärning; Hyperparameterjustering; Linjär Regression; Multipel Linjär Regression; Supervised Learning; Random Forest; Decision Trees;

    Sammanfattning : Efficient price management is crucial for companies with many different products to keep track of, leading to the common practice of price logging. Today, these prices are often adjusted manually, but setting prices manually can be labor-intensive and prone to human error. LÄS MER

  3. 2. Learning a Grasp Prediction Model for Forestry Applications

    Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för fysik

    Författare :Elias Olofsson; [2024]
    Nyckelord :Forwarder; Autonomous grasping; Deep learning; Multibody dynamics; Convolutional neural network;

    Sammanfattning : Since the advent of machine learning and machine vision methods, progress has been made in tackling the long-standing research question of autonomous grasping of arbitrary objects using robotic end-effectors. Building on these efforts, we focus on a subset of the general grasping problem concerning the automation of a forwarder. LÄS MER

  4. 3. Self-Supervised Learning for Tabular Data: Analysing VIME and introducing Mix Encoder

    Kandidat-uppsats, Lunds universitet/Fysiska institutionen

    Författare :Max Svensson; [2024]
    Nyckelord :Machine Learning; Self-supervised learning; AI; Physics; Medicine; Physics and Astronomy;

    Sammanfattning : We introduce Mix Encoder, a novel self-supervised learning framework for deep tabular data models based on Mixup [1]. Mix Encoder uses linear interpolations of samples with associated pretext tasks to form useful pre-trained representations. LÄS MER

  5. 4. Virtual H&E Staining Using PLS Microscopy and Neural Networks

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Sally Vizins; Hanna Råhnängen; [2024]
    Nyckelord :Deep learning; Virtual staining; Skin tissue; Hematoxylin Eosin; H E; Pathology; Carcinoma; Point light source illumination; Neural Networks; GANs; Generative adversarial networks; CNNs; Convolutional neural networks; Relativistic generative adversarial network; Unet; Digital microscopy; Attention-Unet; Dense-Unet; Mathematics and Statistics;

    Sammanfattning : Histopathological examination, crucial in diagnosing diseases such as cancer, traditionally relies on time- and resource-consuming, poorly standardized chemical staining for tissue visualization. This thesis presents a novel digital alternative using generative neural networks and a point light source (PLS) microscope to transform unstained skin tissue images into their stained counterparts. LÄS MER

  7. 5. Domain Adaptation for Multi-Contrast Image Segmentation in Cardiac Magnetic Resonance Imaging

    Master-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)

    Författare :Thomas Proudhon; [2023]
    Nyckelord :Cardiac Magnetic Resonance Imaging; Deep Learning; Domain Adaptation; Unsupervised Segmentation; Image-to-image Translation;

    Sammanfattning : Accurate segmentation of the ventricles and myocardium on Cardiac Magnetic Resonance (CMR) images is crucial to assess the functioning of the heart or to diagnose patients suffering from myocardial infarction. However, the domain shift existing between the multiple sequences of CMR data prevents a deep learning model trained on a specific contrast to be used on a different sequence. LÄS MER