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Siamese networks for similarity learning – Complete Phd and Masters Thesis

[ad_1] **Introduction** Siamese networks have gained significant attention in recent years for their ability to learn similarity between pairs of inputs in a variety of domains such as image recognition, natural language processing, and recommendation…

Capsule networks for part-whole relationships – Complete Phd and Masters Thesis

[ad_1] Introduction Chapter 1: Introduction 1.1 The 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…

Residual networks for deep learning – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, deep learning has revolutionized various fields such as computer vision, natural language processing, and speech recognition. One of the challenges in deep learning is training very deep neural networks, as…

Gated recurrent units for information flow control – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, there has been a growing interest in the use of recurrent neural networks (RNNs) for various applications such as natural language processing, speech recognition, and time series prediction. One popular…

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…

Memory networks for long-term dependencies – Complete Phd and Masters Thesis

[ad_1] Introduction Memory networks have become a popular research topic in the field of machine learning and artificial intelligence due to their ability to capture and store long-term dependencies in sequential data. Traditional neural networks…

Normalizing flows for flexible density estimation – Complete Phd and Masters Thesis

[ad_1] Introduction Normalizing flows have emerged as a powerful tool for flexible density estimation in recent years. These methods allow for the modeling of complex, multi-modal distributions by transforming a simple base distribution into the…

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…

Multi-agent reinforcement learning for coordination – Complete Phd and Masters Thesis

[ad_1] Introduction Multi-agent reinforcement learning (MARL) is a subfield of artificial intelligence that focuses on developing algorithms and techniques for coordinating multiple autonomous agents to achieve a common goal. The coordination of multiple agents presents…

Decentralized AI for distributed intelligence – Complete Phd and Masters Thesis

[ad_1] Introduction: In recent years, artificial intelligence (AI) has played a significant role in revolutionizing various industries by enabling machines to perform tasks that traditionally required human intelligence. However, the centralized nature of traditional AI…

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