[ad_1] Introduction Variational autoencoders (VAEs) have gained significant attention in the field of generative modeling due to their ability to learn complex distributions and generate realistic samples. This thesis aims to explore the use of…
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[ad_1] Introduction Convolutional neural networks (CNNs) have gained significant attention in recent years due to their exceptional performance in various domains, including computer vision, natural language processing, and speech recognition. CNNs are particularly well-suited for…
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[ad_1] **Introduction** Gradient boosting machines (GBM) have become a popular machine learning technique for building predictive models in various fields such as finance, healthcare, and marketing. GBM is a powerful ensemble learning method that combines…
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[ad_1] Introduction Random forests are a powerful ensemble learning method that has gained popularity in various fields of study, including machine learning, data mining, and bioinformatics. This thesis focuses on understanding the principles behind random…
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[ad_1] Introduction: Decision trees are a popular and widely used machine learning technique that provides an interpretable model for making decisions. This thesis explores the use of decision trees for developing interpretable models, focusing on…
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[ad_1] Introduction: Gaussian processes are a powerful tool in machine learning for function approximation. They offer a flexible framework for modeling complex, non-linear relationships in data, while also providing uncertainty estimates for predictions. In recent…
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[ad_1] Introduction Knowledge representation is a key aspect of intelligent systems, enabling them to understand and reason about the world around them. By encoding information in a form that can be easily processed by algorithms,…
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[ad_1] Introduction Document clustering is the process of grouping similar documents together based on their content or attributes. This technique is widely used in various applications such as information retrieval, text mining, and document organization.…
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[ad_1] Introduction Keyword extraction is a crucial process in document indexing that involves identifying and extracting the most relevant terms from a document to facilitate efficient information retrieval and organization. With the exponential growth of…
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[ad_1] Introduction In recent years, the field of natural language processing (NLP) has seen significant advancements in the use of word embeddings for vector representation. Word embeddings are mathematical representations of words in a continuous…
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