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Video summarization for highlight extraction – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, the explosion of online video content has created a need for automated methods of video summarization for highlight extraction. Video summarization involves condensing the content of a video into a…

Image captioning for visual description generation – Complete Phd and Masters Thesis

[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…

Neural machine translation for end-to-end translation – Complete Phd and Masters Thesis

[ad_1] Introduction Neural machine translation has revolutionized the field of automated translation in recent years. Traditional machine translation systems relied on complex rule-based approaches that struggled with the nuances of language. However, neural machine translation,…

Recurrent neural network language models for sequence prediction – Complete Phd and Masters Thesis

[ad_1] Introduction Recurrent neural networks (RNNs) have gained significant attention in recent years for their ability to model sequential data and make predictions based on this data. In particular, recurrent neural network language models have…

Convolutional deep belief networks for spatial data – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, there has been a growing interest in the development and application of deep learning techniques for the analysis of spatial data. Convolutional Deep Belief Networks (CDBNs) have emerged as a…

Deep belief networks for hierarchical representation – Complete Phd and Masters Thesis

[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…

Hopfield networks for associative memory – Complete Phd and Masters Thesis

[ad_1] Introduction Hopfield networks are a type of recurrent neural network that have been widely used for associative memory tasks. First introduced by John Hopfield in 1982, these networks are capable of storing and recalling…

Generative adversarial networks for realistic data synthesis – Complete Phd and Masters Thesis

[ad_1] Introduction Over the past few years, Generative Adversarial Networks (GANs) have gained significant attention in the field of artificial intelligence and machine learning. GANs are a type of deep neural network architecture that consists…

Variational autoencoders for generative modeling – Complete Phd and Masters Thesis

[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…

Long short-term memory networks for long-term dependencies – Complete Phd and Masters Thesis

[ad_1] Introduction Long short-term memory (LSTM) networks are a type of recurrent neural network (RNN) that have been specifically designed to address the issue of capturing long-term dependencies in sequential data. Traditional RNNs suffer from…

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