Essays about: "Djup lärning."

Showing result 1 - 5 of 18 essays containing the words Djup lärning..

  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. Exploring the Depth-Performance Trade-Off : Applying Torch Pruning to YOLOv8 Models for Semantic Segmentation Tasks

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

    Author : Xinchen Wang; [2024]
    Keywords : Deep Learning; Semantic segmentation; Network optimization; Network pruning; Torch Pruning; YOLOv8; Network Depth; Djup lärning; Semantisk segmentering; Nätverksoptimering; Nätverksbeskärning; Fackelbeskärning; YOLOv8; Nätverksdjup;

    Abstract : In order to comprehend the environments from different aspects, a large variety of computer vision methods are developed to detect objects, classify objects or even segment them semantically. Semantic segmentation is growing in significance due to its broad applications in fields such as robotics, environmental understanding for virtual or augmented reality, and autonomous driving. READ MORE

  4. 3. Heart rate estimation from wrist-PPG signals in activity by deep learning methods

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

    Author : Marie-Ange Stefanos; [2023]
    Keywords : Deep Learning; Medical Data; Signal Processing; Heart Rate Estimation; Wrist Photoplethysmography; Djup lärning; Medicinska Data; Signalbehandling; Pulsuppskattning; Handledsfotopletysmograf;

    Abstract : In the context of health improving, the measurement of vital parameters such as heart rate (HR) can provide solutions for health monitoring, prevention and screening for certain chronic diseases. Among the different technologies for HR measuring, photoplethysmography (PPG) technique embedded in smart watches is the most commonly used in the field of consumer electronics since it is comfortable and does not require any user intervention. READ MORE

  5. 4. Fair NFTs evaluation based on historical sales, market data and NFTs metadata

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

    Author : Marcello Rigotti; [2023]
    Keywords : Blockchain; NFT; Artificial Intelligence; Deep Learning; Blockchain; NFT; Artificiell Intelligens; Djup lärning;

    Abstract : Blockchain technology is rapidly growing and with it, the opportunities it brings. Non-fungible tokens (NFTs) are a type of token that represents unique data that can be owned and traded on a blockchain. READ MORE

  6. 5. Reverse Engineering of Deep Learning Models by Side-Channel Analysis

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

    Author : Xuan Wang; [2023]
    Keywords : Side-Channel Attack; Deep Learning; Reverse Engineering; Perceptron Neural Network; Sidokanalattack; Djup lärning; Omvänd Konstruktion; Perceptron Neurala Nätverk;

    Abstract : Side-Channel Analysis (SCA) aims to extract secrets from cryptographic systems by exploiting the physical leakage acquired from implementations of cryptographic algorithms. With the development of Deep Learning (DL), a new type of SCA called Deep Learning Side-Channel Analysis (DLSCA) utilizes the advantages of DL techniques in data features processing to break cryptographic systems more efficiently. READ MORE