Essays about: "Maskininlärning ML"

Showing result 1 - 5 of 109 essays containing the words Maskininlärning ML.

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

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

    Author : Abel Getachew Kenea; Gabriel Fagerslett; [2024]
    Keywords : 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;

    Abstract : 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. READ MORE

  3. 2. 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. 3. 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. 4. Utilizing energy-saving techniques to reduce energy and memory consumption when training machine learning models : Sustainable Machine Learning

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

    Author : Khalid El Yaacoub; [2024]
    Keywords : 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;

    Abstract : 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. READ MORE

  6. 5. Analyzing the performance of active learning strategies on machine learning problems

    University essay from Uppsala universitet/Avdelningen för systemteknik

    Author : Vendela Werner; [2023]
    Keywords : computer science; bioinformatics; machine learning; active learning; artificial intelligence; supervised learning; Astrazeneca; maskininlärning; artificiell intelligens; datorvetenskap; active learning; bioinformatik; supervised learning;

    Abstract : Digitalisation within industries is rapidly advancing and data possibilities are growing daily. Machine learning models need a large amount of data that are well-annotated for good performance. To get well-annotated data, an expert is needed, which is expensive, and the annotation itself could be very time-consuming. READ MORE