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Neuromorphic computing for brain-inspired architectures – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, there has been a growing interest in neuromorphic computing, a field that seeks to imitate the structure and functionality of the human brain in developing computer architectures. Inspired by the…

Bayesian neural networks for uncertainty estimation – Complete Phd and Masters Thesis

[ad_1] Introduction Bayesian neural networks (BNNs) have gained significant attention in recent years due to their ability to provide uncertainty estimates in neural network models. This is crucial in various applications such as medical diagnosis,…

Mixture of experts for specialized sub-networks – Complete Phd and Masters Thesis

[ad_1] Introduction Mixture of experts for specialized sub-networks is a cutting-edge approach in the field of machine learning and artificial intelligence that aims to improve the performance of neural networks by combining multiple specialized sub-networks…

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…

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…

Inception networks for multi-scale feature extraction – Complete Phd and Masters Thesis

[ad_1] Introduction: In recent years, deep learning has revolutionized the field of computer vision by achieving unprecedented levels of accuracy in various tasks, such as image classification, object detection, and segmentation. One key component of…

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…

Long short-term memory networks for sequence modeling – Complete Phd and Masters Thesis

[ad_1] Introduction Long short-term memory (LSTM) networks have gained significant attention in the field of neural networks due to their ability to model complex sequences and learn long-term dependencies. These networks have been successfully applied…

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…

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