Sökning: "Machine learning implementation"

Visar resultat 1 - 5 av 438 uppsatser innehållade orden Machine learning implementation.

  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 In-Depth study on the Utilization of Large Language Models for Test Case Generation

    Master-uppsats, Umeå universitet/Institutionen för datavetenskap

    Författare :Nicole Johnsson; [2024]
    Nyckelord :Large Language Models; Test Case Generation; Retrieval Augmented Generation; Machine Learning; Generative AI;

    Sammanfattning : This study investigates the utilization of Large Language Models for Test Case Generation. The study uses the Large Language model and Embedding model provided by Llama, specifically Llama2 of size 7B, to generate test cases given a defined input. LÄS MER

  4. 3. Undersökning av metoder för automatiserad kontinuerlig datautvinning av IoT-data för att utvinna funktioner

    M1-uppsats, KTH/Hälsoinformatik och logistik

    Författare :Erik Järte; [2024]
    Nyckelord :IoT; user data; machine learning; visualization; data analysis; IoT; användardata; maskininlärning; visualisering; dataanalys;

    Sammanfattning : Företaget Cake har idag inte en komplett bild över hur dess fordon används. Därför samlar företaget idag upp användardata i en förhoppning om att kunna analysera denna data för att få insikter över hur dess produkter används och vad de ska satsa på i framtiden. LÄS MER

  5. 4. Machine Learning for Spatial Positioning for XR Environments

    Kandidat-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

    Författare :Khaled Alraas; [2024]
    Nyckelord :Extended Reality; Machine Learning; Sensor Fusion; Spatial Data Accuracy Virtual Productions; Augmented Reality; Virtual Reality; Real-time Camera Tracking; Location-Based Services; Gaming Platforms; Sensor Integration;

    Sammanfattning : This bachelor's thesis explores the integration of machine learning (ML) with sensor fusion techniques to enhance spatial data accuracy in Extended Reality (XR) environments. With XR's revolutionary impact across various sectors, accurate localization in virtual environments becomes imperative. LÄS MER

  7. 5. Utilizing energy-saving techniques to reduce energy and memory consumption when training machine learning models : Sustainable Machine Learning

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

    Författare :Khalid El Yaacoub; [2024]
    Nyckelord :Sustainable AI; Machine learning; Quantization-Aware Training; Model Distillation; Quantized Distillation; Siamese Neural Networks; Continual Learning; Experience Replay; Data Efficient AI; Energy Consumption; Energy-Savings; Sustainable ML; Computation resources; Hållbar maskin inlärning; Hållbar AI; Maskininlärning; Quantization-Aware Training; Model Distillation; Quantized Distillation; siamesiska neurala nätverk; Continual Learning; Experience Replay; Dataeffektiv AI; Energiförbrukning; Energibesparingar; Beräkningsresurser;

    Sammanfattning : Emerging machine learning (ML) techniques are showing great potential in prediction performance. However, research and development is often conducted in an environment with extensive computational resources and blinded by prediction performance. LÄS MER