Essays about: "Artificiellt neurala nätverk"
Showing result 1 - 5 of 14 essays containing the words Artificiellt neurala nätverk.
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1. Modelling Long Term Memory in the Bayesian Confidence Neural Network Model
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Memory is a fascinating and complex part of human life. Understanding memory and simulating itthrough modelling can help society take steps towards understanding health issues such asAlzheimer's, dementia and amnesia. READ MORE
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2. The data-driven CyberSpine : Modeling the Epidural Electrical Stimulation using Finite Element Model and Artificial Neural Networks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Every year, 250,000 people worldwide suffer a spinal cord injury (SCI) that leaves them with chronic paraplegia - permanent loss of ability to move their legs. SCI interrupts axons passing along the spinal cord, thereby isolating motor neurons from brain inputs. To date, there are no effective treatments that can reconnect these interrupted axons. READ MORE
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3. LDPC DropConnect
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Machine learning is a popular topic that has become a scientific research tool in many fields. Overfitting is a common challenge in machine learning, where the model fits the training data too well and performs poorly on new data. READ MORE
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4. Learning Based Road Estimation
University essay from Lunds universitet/Matematik LTHAbstract : The interest in autonomous driving has vastly increased, leading to a surge in research and development efforts over the past decades. This technology could enhance road safety, alleviate traffic congestion, and yield numerous environmental and economic benefits. READ MORE
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5. Deep neural networks for food waste analysis and classification : Subtraction-based methods for the case of data scarcity
University essay from Uppsala universitet/Signaler och systemAbstract : Machine learning generally requires large amounts of data, however data is often limited. On the whole the amount of data needed grows with the complexity of the problem to be solved. Utilising transfer learning, data augmentation and problem reduction, acceptable performance can be achieved with limited data for a multitude of tasks. READ MORE