Sökning: "Fashion Prediction"

Visar resultat 1 - 5 av 22 uppsatser innehållade orden Fashion Prediction.

1. 1. Learning Embeddings for Fashion Images

   Master-uppsats, Linköpings universitet/Datorseende

   Författare :Simon Hermansson; [2023]
   Nyckelord :Computer Vision; Machine Learning; Image Retrieval; CLIP; Masked Autoencoders MAE ; Vision Transformers; Image Captioning; Price Prediction; AI for Fashion;

   Sammanfattning : Today the process of sorting second-hand clothes and textiles is mostly manual. In this master’s thesis, methods for automating this process as well as improving the manual sorting process have been investigated. LÄS MER

3. 2. Customer churn prediction in a slow fashion e-commerce context : An analysis of the effect of static data in customer churn prediction

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

   Författare :Luca Colasanti; [2023]
   Nyckelord :Survival Analysis; Time To Event prediction; Churn retention; Machine Learning; Deep Learning; Customer Clustering; E-commerce; Analisi di sopravvivenza; Previsione del tempo a evento; Ritenzione dall’abbandono dei clienti; Apprendimento automatico; Apprendimento profondo; Segmentazione della clientela; Commercio elettronico; Överlevnadsanalys; Tid till händelseförutsägelse; Churn Prediction; Maskininlärning; Djuplärning; Kundkluster; E-handel;

   Sammanfattning : Survival analysis is a subfield of statistics where the goal is to analyse and model the data where the outcome is the time until the occurrence of an event of interest. Because of the intrinsic temporal nature of the analysis, the employment of more recently developed sequential models (Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM)) has been paired with the use of dynamic temporal features, in contrast with the past reliance on static ones. LÄS MER

4. 3. Electron-skyrmion systems, in and out of equilibrium, and isolated or contacted to reservoirs

   Magister-uppsats, Lunds universitet/Matematisk fysik; Lunds universitet/Fysiska institutionen

   Författare :Emil Adam Jonatan Östberg; [2023]
   Nyckelord :skyrmion; skyrmions; NEGF; freen s function; quantum transport; non-equlibrium; topology; topological charge; skyrmionics; current induced; open system; Physics and Astronomy;

   Sammanfattning : A Kondo lattice skyrmion model in contact with a macroscopic environment is simulated to explore skyrmion dynamics, which is an extension of previous work. The system is simulated using non-equilibrium Green's functions within the generalized Kadanoff-Baym ansatz and the wide band limit. LÄS MER

5. 4. Trainable Region of Interest Prediction: Hard Attention Framework for Hardware-Efficient Event-Based Computer Vision Neural Networks on Neuromorphic Processors

   Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik

   Författare :Cina Arjmand; [2023]
   Nyckelord :Artifical Intelligence; Machine Learning; Neuromorphic Engineering; Computer Vision; Technology and Engineering;

   Sammanfattning : Neuromorphic processors are a promising new type of hardware for optimizing neural network computation using biologically-inspired principles. They can effectively leverage information sparsity such as in images from event-based cameras, and are well-adapted to processing event-based data in an energy-efficient fashion. LÄS MER

7. 5. Deep learning, LSTM and Representation Learning in Empirical Asset Pricing

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

   Författare :Benjamin von Essen; [2022]
   Nyckelord :LSTM; empirical asset pricing; deep learning; representation learning; neural networks; LSTM; empirisk tillgångsvärdering; djupinlärning; representationsinlärning; neurala nätverk;

   Sammanfattning : In recent years, machine learning models have gained traction in the field of empirical asset pricing for their risk premium prediction performance. In this thesis, we build upon the work of [1] by first evaluating models similar to their best performing model in a similar fashion, by using the same dataset and measures, and then expanding upon that. LÄS MER