Sökning: "batch learning"

Visar resultat 1 - 5 av 70 uppsatser innehållade orden batch 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. An Empirical Survey of Bandits in an Industrial Recommender System Setting

   Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

   Författare :Tobias Schwarz; Johan Brandby; [2023-09-21]
   Nyckelord :computer science; industrial application; machine learning; reinforcement learning; multi-armed bandits; MAB; contextual multi-armed bandits; survey; batch learning;

   Sammanfattning : In this thesis, the effects of incorporating unstructured data—images in the wild—in contextual multi-armed bandits are investigated, when used within a recommender system setting, which focuses on picture-based content suggestion. The idea is to employ image features, extracted by a pre-trained convolutional neural network, and study the resulting bandit behaviors when including respective excluding this information in the typical context creation, which normally relies on structured data sources—such as metadata. LÄS MER

4. 3. Natural Language Inference Transfer Learning in a Multi-Task Contract Dataset : In the Case of ContractNLI: a Document Information Extraction System

   Master-uppsats, Uppsala universitet/Institutionen för lingvistik och filologi

   Författare :Yiu Kei Tang; [2023]
   Nyckelord :;

   Sammanfattning : This thesis investigates the enhancement of legal contract Natural Language Inference (NLI) classification through supervised fine-tuning on general domain NLI, in the case of ContractNLI and Span NLI BERT (Koreeda and Manning, 2021), a multi-task document information extraction dataset and framework. Annotated datasets of a specific professional domain are scarce due to the high time and labour cost required to create them. LÄS MER

5. 4. Improved U-Net architecture for Crack Detection in Sand Moulds

   Kandidat-uppsats, Högskolan i Gävle/Datavetenskap

   Författare :Husain Ahmed; Hozan Bajo; [2023]
   Nyckelord :U-Net Architecture; Semantic Segmentation; Convolutional Neural Networks; Crack Detection;

   Sammanfattning : The detection of cracks in sand moulds has long been a challenge for both safety and maintenance purposes. Traditional image processing techniques have been employed to identify and quantify these defects but have often proven to be inefficient, labour-intensive, and time-consuming. LÄS MER

7. 5. Using Machine Learning to Optimize Near-Earth Object Sighting Data at the Golden Ears Observatory

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

   Författare :Laura Murphy; [2023]
   Nyckelord :Near-Earth Object Detection; Machine Learning; Deep Learning; Visual Transformers;

   Sammanfattning : This research project focuses on improving Near-Earth Object (NEO) detection using advanced machine learning techniques, particularly Vision Transformers (ViTs). The study addresses challenges such as noise, limited data, and class imbalance. LÄS MER