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Self-attention networks for global dependencies – Complete Phd and Masters Thesis

[ad_1] Introduction: Self-attention networks have gained significant attention in the field of artificial intelligence and machine learning due to their ability to capture global dependencies in data. These networks have shown promising results in various…

Triplet networks for comparative learning – Complete Phd and Masters Thesis

[ad_1] Introduction Triplet networks have gained popularity in recent years for their ability to learn effective representations for various tasks, particularly in the field of comparative learning. In a triplet network, the model is trained…

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…

Recurrent neural networks for temporal data – Complete Phd and Masters Thesis

[ad_1] Introduction: Recurrent neural networks (RNNs) have gained significant attention in recent years due to their ability to effectively model sequential data. In particular, RNNs are well-suited for analyzing temporal data, where there is a…

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…

Transformers for sequence modeling – Complete Phd and Masters Thesis

[ad_1] Introduction: Chapter 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…

Autoregressive models for sequential generation – Complete Phd and Masters Thesis

[ad_1] Introduction Autoregressive models have gained significant attention in recent years due to their ability to generate realistic sequences of data, such as text, audio, and images. These models have been successful in various applications,…

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…

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