Search for dissertations about: "Neural Network training"
Showing result 1 - 5 of 109 swedish dissertations containing the words Neural Network training.
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1. Neural Network Architecture Design : Towards Low-complexity and Scalable Solutions
Abstract : Over the past few years, deep neural networks have been at the center of attention in machine learning literature thanks to the advances in computational capabilities of modern graphical processing units (GPUs). This progress has made it possible to train large scale neural networks by using thousands, and even millions, of training samples to achieve outstanding estimation accuracy in various applications that were not simply possible before. READ MORE
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2. Network Parameterisation and Activation Functions in Deep Learning
Abstract : Deep learning, the study of multi-layered artificial neural networks, has received tremendous attention over the course of the last few years. Neural networks are now able to outperform humans in a growing variety of tasks and increasingly have an impact on our day-to-day lives. READ MORE
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3. Neural networks in context: challenges and opportunities : a critical inquiry into prerequisites for user trust in decisions promoted by neural networks
Abstract : Artificial intelligence and machine learning (ML) in particular increasingly impact human life by creating value from collected data. This assetisation affects all aspectsof human life, from choosing a significant other to recommending a product for us to consume. READ MORE
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4. Characterizing Piecewise Linear Neural Networks
Abstract : Neural networks utilizing piecewise linear transformations between layers have in many regards become the default network type to use across a wide range of applications. Their superior training dynamics and generalization performance irrespective of the nature of the problem has resulted in these networks achieving state of the art results on a diverse set of tasks. READ MORE
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5. Neural Network Approaches To Survival Analysis
Abstract : Predicting the probable survival for a patient can be very challenging for many diseases. In many forms of cancer, the choice of treatment can be directly impacted by the estimated risk for the patient. This thesis explores different methods to predict the patient's survival chances using artificial neural networks (ANN). READ MORE