Sökning: "Djuplärning"

Hittade 4 uppsatser innehållade ordet Djuplärning.

1. 1. 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

3. 2. Training a Neural Network using Synthetically Generated Data

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

   Författare :Fredrik Diffner; Hovig Manjikian; [2020]
   Nyckelord :Synthetic data set; Generating synthetic data set; Machine learning; Deep Learning; Convolutional Neural Networks; Machine learning model; Character recognition in natural images; Char74k; ICDAR2003.; Syntetiskt dataset; Generera syntetiskt data; Maskininlärning; Maskininlärningsmodell; Djuplärning; Konvolutionära neurala nätverk; teckenigenkänning i bilder; Char74k; ICDAR2003;

   Sammanfattning : A major challenge in training machine learning models is the gathering and labeling of a sufficiently large training data set. A common solution is the use of synthetically generated data set to expand or replace a real data set. LÄS MER

4. 3. Identifiera löv i skogar – Att lära en dator känna igen löv med ImageAI

   Kandidat-uppsats, Mittuniversitetet/Institutionen för informationssystem och –teknologi

   Författare :My Nordqvist; [2019]
   Nyckelord :SCA; Machine learning; Neural networks; Deep learning; Forest classification; Leaf; Orthophoto; Python; TensorFlow; ImageAI; SCA; Maskininlärning; Neurala nätverk; Djuplärning; Klassificering av skog; Löv; Ortofoton; Python; TensorFlow; ImageAI;

   Sammanfattning : A current field of research today is machine learning because it can simplify everyday life for human beings. A functioning system that has learned specific tasks can make it easier for companies in both cost and time. A company who want to use machine learning is SCA, who owns and manages forests to produce products. LÄS MER

5. 4. Mobile Object Detection using TensorFlow Lite and Transfer Learning

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

   Författare :Oscar Alsing; [2018]
   Nyckelord :cnn; convolutional neural networks; transfer learning; mobile object detection;

   Sammanfattning : With the advancement in deep learning in the past few years, we are able to create complex machine learning models for detecting objects in images, regardless of the characteristics of the objects to be detected. This development has enabled engineers to replace existing heuristics-based systems in favour of machine learning models with superior performance. LÄS MER