Sökning: "olika inlärningsmodeller"

Visar resultat 1 - 5 av 10 uppsatser innehållade orden olika inlärningsmodeller.

1. 1. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition

   Master-uppsats, KTH/Mekatronik och inbyggda styrsystem

   Författare :Helgi Hrafn Björnsson; Jón Kaldal; [2023]
   Nyckelord :Recurrent Neural Networks; Long Short-Term Memory Networks; Embedded Systems; Human Activity Recognition; Edge AI; TensorFlow Lite Micro; Recurrent Neural Networks; Long Short-Term Memory Networks; Innbyggda systyem; Mänsklig aktivitetsigenkänning; Edge AI; TensorFlow Lite Micro;

   Sammanfattning : Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. LÄS MER

3. 2. Improving the Robustness of Deep Neural Networks against Adversarial Examples via Adversarial Training with Maximal Coding Rate Reduction

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

   Författare :Hsiang-Yu Chu; [2022]
   Nyckelord :Machine learning; Deep neural networks; Loss function; Adversarial example; Adversarial attack; Adversarial training; Maskininlärning; Djupa neurala nätverk; Förlustfunktion; Motståndarexempel; Motståndarattack; Motståndsträning;

   Sammanfattning : Deep learning is one of the hottest scientific topics at the moment. Deep convolutional networks can solve various complex tasks in the field of image processing. However, adversarial attacks have been shown to have the ability of fooling deep learning models. LÄS MER

4. 3. Decentralized Large-Scale Natural Language Processing Using Gossip Learning

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

   Författare :Abdul Aziz Alkathiri; [2020]
   Nyckelord :gossip learning; decentralized machine learning; distributed machine learning; NLP; Word2Vec; data privacy; skvallerinlärning; decentraliserad maskininlärning; distribuerad maskininlärning; naturlig språkbehandling; Word2Vec; dataintegritet;

   Sammanfattning : The field of Natural Language Processing in machine learning has seen rising popularity and use in recent years. The nature of Natural Language Processing, which deals with natural human language and computers, has led to the research and development of many algorithms that produce word embeddings. LÄS MER

5. 4. A Deep Learning Approach to Predicting the Length of Stay of Newborns in the Neonatal Intensive Care Unit

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

   Författare :Bas Theodoor Straathof; [2020]
   Nyckelord :Deep Neural Networks; Electronic Health Records; Length-of-Stay Prediction; Multivariate Time Series Classification; Djupa Neurala Nätverk; Elektroniska Hälsoregister; Klassificering av Multivariat Tidsserie; Förutsägelse av Vistelsetid;

   Sammanfattning : Recent advancements in machine learning and the widespread adoption of electronic healthrecords have enabled breakthroughs for several predictive modelling tasks in health care. One such task that has seen considerable improvements brought by deep neural networks is length of stay (LOS) prediction, in which research has mainly focused on adult patients in the intensive care unit. LÄS MER

7. 5. Predicting House Prices on the Countryside using Boosted Decision Trees

   Master-uppsats, KTH/Matematisk statistik

   Författare :War Revend; [2020]
   Nyckelord :Machine Learning; Predicting House Prices; Shrinkage Methods; Random Forest; Decision Tree; AdaBoost; Gradient Boosting; LightGBM; CatBoost; XGBoost; Maskininlärning; Förutseende av Huspriser; Krympningsmetoder; Random Forest; Beslutsträd; AdaBoost; Gradient Boosting; LightGBM; CatBoost; XGBoost;

   Sammanfattning : This thesis intends to evaluate the feasibility of supervised learning models for predicting house prices on the countryside of South Sweden. It is essential for mortgage lenders to have accurate housing valuation algorithms and the current model offered by Booli is not accurate enough when evaluating residence prices on the countryside. LÄS MER