[ad_1] Introduction In recent years, deep belief networks (DBNs) have gained significant attention in the field of artificial intelligence and machine learning due to their ability to learn hierarchical representations of data. These networks are…
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[ad_1] Introduction Self-organizing maps (SOMs) have been widely used in various fields such as machine learning, data visualization, pattern recognition, and clustering. One of the key advantages of SOMs is their ability to preserve the…
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[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 Autoencoders have gained significant attention in recent years as powerful tools for unsupervised representation learning. These neural networks are capable of learning compact and meaningful representations of data without the need for labeled…
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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 Support vector machines (SVM) are powerful machine learning algorithms that have gained popularity in recent years due to their ability to handle both classification and regression tasks effectively. SVM works by finding the…
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[ad_1] Introduction: Particle filters are a powerful tool in the field of sequential Monte Carlo methods for estimating the state of a dynamic system based on noisy observations. These filters are widely used in a…
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[ad_1] Thesis Overview Title: Partially Observable Markov Decision Processes for Imperfect Information Introduction: Partially Observable Markov decision processes (POMDPs) are a powerful framework for modeling decision-making problems in the presence of uncertainty. In many real-world…
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[ad_1] Introduction 1.1 Introduction 1.2 Background of Study 1.3 Problem Statement 1.4 Objective of Study 1.5 Limitation of Study 1.6 Scope of Study 1.7 Significance of Study 1.8 Structure of the Thesis 1.9 Definition of…
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[ad_1] Introduction Information extraction is the process of automatically extracting structured knowledge from unstructured or semi-structured sources. It plays a critical role in various fields such as natural language processing, data mining, and knowledge management.…
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