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Convolutional neural networks for grid-like data – Complete Phd and Masters Thesis

[ad_1] Introduction Convolutional neural networks (CNNs) have gained significant importance in recent years for their ability to effectively extract and learn features from grid-like data such as images, videos, and sensor data. This thesis focuses…

Graph neural networks for structured data – Complete Phd and Masters Thesis

[ad_1] Introduction Graph neural networks have emerged as a powerful tool for analyzing and modeling structured data such as social networks, protein-protein interaction networks, and citation networks. Unlike traditional neural networks that operate on grid-like…

Variational autoencoders for latent representation – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, deep learning models have shown impressive performance in various tasks such as image recognition, natural language processing, and speech recognition. Variational autoencoders (VAEs) are a type of generative model that…

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

[ad_1] Introduction: Generative adversarial networks (GANs) have emerged as powerful tools for generating realistic synthetic data in recent years. By pitting two neural networks against each other in a zero-sum game setting, GANs are able…

Swarm intelligence for collective behavior – Complete Phd and Masters Thesis

[ad_1] **Introduction** Swarm intelligence is a field of study that focuses on the collective behavior of decentralized, self-organizing systems. It draws inspiration from the behavior of social insects such as ants, bees, and termites, which…

Smart contracts for automated machine learning – Complete Phd and Masters Thesis

[ad_1] Introduction Smart contracts have gained popularity in recent years due to their ability to automate and facilitate secure transactions on blockchain platforms. In the field of machine learning, smart contracts can be used to…

Homomorphic encryption for privacy-preserving computation – Complete Phd and Masters Thesis

[ad_1] Introduction Homomorphic encryption is a groundbreaking technology that allows for computations to be performed on encrypted data without the need to decrypt it. This offers a powerful tool for privacy-preserving computation, ensuring that sensitive…

Convex optimization for global solutions – Complete Phd and Masters Thesis

[ad_1] Introduction Convex optimization is a powerful mathematical tool that has been widely used in various fields such as machine learning, signal processing, control systems, and operations research. It involves the optimization of convex objective…

Submodular optimization for diversity and coverage – Complete Phd and Masters Thesis

[ad_1] Introduction: In recent years, there has been a growing interest in submodular optimization for diversity and coverage in various fields such as machine learning, data mining, and artificial intelligence. Submodular functions have the property…

Collaborative filtering for recommendation – Complete Phd and Masters Thesis

[ad_1] Introduction Collaborative filtering is a popular technique used in recommendation systems to provide personalized suggestions to users based on their preferences and behaviors. With the increasing amount of information available online, the need for…

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