Essays about: "hyper-parameter optimization"
Showing result 6 - 10 of 12 essays containing the words hyper-parameter optimization.
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6. Extraction and Quantification of Features in XCT Datasets of Fibre Reinforced Polymers using Machine Learning Techniques
University essay from Umeå universitet/Institutionen för datavetenskapAbstract : This master’s thesis shows the extraction, quantification and visual analysis of pores and individual fibres in fibre reinforced polymer (FRP)materials. The core methods used and advanced for this purpose are tailored deep learning techniques, which are coupled with interactive visualisation. READ MORE
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7. Comparing Machine Learning Algorithms and Feature Selection Techniques to Predict Undesired Behavior in Business Processesand Study of Auto ML Frameworks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : In recent years, the scope of Machine Learning algorithms and its techniques are taking up a notch in every industry (for example, recommendation systems, user behavior analytics, financial applications and many more). In practice, they play an important role in utilizing the power of the vast data we currently generate on a daily basis in our digital world. READ MORE
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8. Evaluating Random Forest and a Long Short-Term Memory in Classifying a Given Sentence as a Question or Non-Question
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Natural language processing and text classification are topics of much discussion among researchers of machine learning. Contributions in the form of new methods and models are presented on a yearly basis. However, less focus is aimed at comparing models, especially comparing models that are less complex to state-of-the-art models. READ MORE
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9. Anomaly Detection in Categorical Data with Interpretable Machine Learning : A random forest approach to classify imbalanced data
University essay from Linköpings universitet/Statistik och maskininlärningAbstract : Metadata refers to "data about data", which contains information needed to understand theprocess of data collection. In this thesis, we investigate if metadata features can be usedto detect broken data and how a tree-based interpretable machine learning algorithm canbe used for an effective classification. The goal of this thesis is two-fold. READ MORE
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10. A Comparative Study of Black-box Optimization Algorithms for Tuning of Hyper-parameters in Deep Neural Networks
University essay from Luleå tekniska universitet/Institutionen för teknikvetenskap och matematikAbstract : Deep neural networks (DNNs) have successfully been applied across various data intensive applications ranging from computer vision, language modeling, bioinformatics and search engines. Hyper-parameters of a DNN are defined as parameters that remain fixed during model training and heavily influence the DNN performance. READ MORE
