Sökning: "Deep learning models"

Visar resultat 1 - 5 av 931 uppsatser innehållade orden Deep learning models.

1. 1. Station-level demand prediction in bike-sharing systems through machine learning and deep learning methods

   Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Författare :Nikolaos Staikos; [2024]
   Nyckelord :Physical Geography; Ecosystem Analysis; Bike-sharing demand; Machine learning; Deep learning; Spatial regression; Graph Convolutional Neural Network; Multiple Linear Regression; Multilayer Perceptron Regressor; Support Vector Machine; Random Forest Regressor; Urban environment; Micro-mobility; Station planning; Geomatics; Earth and Environmental Sciences;

   Sammanfattning : Public Bike-Sharing systems have been employed in many cities around the globe. Shared bikes are an efficient and convenient means of transportation in advanced societies. Nonetheless, station planning and local bike-sharing network effectiveness can be challenging. LÄS MER

3. 2. 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

4. 3. Attack Strategies in Federated Learning for Regression Models : A Comparative Analysis with Classification Models

   Master-uppsats, Umeå universitet/Institutionen för datavetenskap

   Författare :Sofia Leksell; [2024]
   Nyckelord :Federated Learning; Adversarial Attacks; Regression; Classification;

   Sammanfattning : Federated Learning (FL) has emerged as a promising approach for decentralized model training across multiple devices, while still preserving data privacy. Previous research has predominantly concentrated on classification tasks in FL settings, leaving  a noticeable gap in FL research specifically for regression models. LÄS MER

5. 4. Transforming Chess: Investigating Decoder-Only Architecture for Generating Realistic Game-Like Positions

   Master-uppsats, Umeå universitet/Institutionen för datavetenskap

   Författare :William Pettersson; [2024]
   Nyckelord :Transformer; Decoder; Decoder-only; AI; Artificial intelligence; Generating; generative model; Chess;

   Sammanfattning : Chess is a deep and intricate game, the master of which depends on learning tens of thousands of the patterns that may occur on the board. At Noctie, their mission is to aid this learning process through humanlike chess AI. A prominent challenge lies in curating instructive chess positions for students. LÄS MER

7. 5. 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