Deep Learning Introduction to Multi-Agent Systems (MAS) Multi-Agent Systems (MAS) consist of autonomous agents that interact with each other and their environment to achieve individual or collective goals.
Deep Learning Neural Scaling Law: A Brief Introduction Neural scaling law is a term that describes how the performance of a neural network model depends on various factors such as the size of the model, the size of the training dataset, the cost of training, and the complexity of the task.
Deep Learning Deep Learning for traffic prediction [project with source code] This article at OpenGenus explores the development of a Deep Learning (DL) traffic predictor using a comprehensive dataset.
Deep Learning Understanding AdaDelta: An Adaptive Learning Rate Optimization Algorithm In this article at OpenGenus, we will explore AdaDelta from the ground up, understanding its mechanics and the benefits it offers over traditional optimization techniques.
Deep Learning He initialization in Deep Learning He initialization, also known as Kaiming Initialization, is a widely used technique in deep learning for initializing the weights of neural networks. It was introduced by Kaiming He et al. in 2015 as an improvement over the traditional random initialization methods.
Deep Learning 7 Different Prompting Techniques In this article at OpenGenus, we will explore various techniques utilized in prompt engineering, shedding light on the most popular and effective approaches.
Deep Learning Squeeze and Excitation (SE) Block The SE block focuses specifically on improving the channel relationship. It introduces a mechanism to capture and emphasize important channel-wise information, enabling CNNs to better discriminate and learn relevant features.
Deep Learning Use of Deep Learning for VR/AR In recent years, deep learning has begun to be used in VR/AR applications. This is because deep learning can be used to improve the realism, interaction, and immersion of VR/AR experiences.
Machine Learning (ML) Hyperplane in SVM In SVMs, a hyperplane is a subspace of one dimension less than the original feature space. In two-dimensional space, a hyperplane is a line, while in three-dimensional space, it is a plane.