Learn With Jay on MSN

Backpropagation in neural networks step-by-step explained

Understand the Maths behind Backpropagation in Neural Networks. In this video, we will derive the equations for the Back ...
Learn With Jay on MSN

Python neural network for multiclass classification using softmax

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by ...

Predictive Modeling: Techniques, Uses, and Key Takeaways

Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.
IEEE

SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings

Abstract: Deep neural networks for image super-resolution (SR) have demonstrated superior performance. However, the large memory and computation consumption hinders their deployment on ...
C&EN

Leveraging Deep Learning to Address Diagnostic Challenges with Insufficient Image Data

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. In recent AI-driven disease diagnosis, the success of models has depended mainly on ...
Phys.org

Researchers develop neural network for large-scale celestial object classification

A new study led by researchers from the Yunnan Observatories of the Chinese Academy of Sciences has developed a neural network-based method for large-scale celestial object classification, according ...
Scientific Research Publishing

Amin, F. and Mahmoud, M. (2022) Confusion Matrix in Binary Classification Problems: A Step-by-Step Tutorial. Journal of Engineering Research, 6, Article No. 1.

ABSTRACT: Accurate prediction of malaria incidence is indispensable in helping policy makers and decision makers intervene before the onset of an outbreak and potentially save lives. Various ...
INSPIRE

Exploring quantum neural networks for binary classification on MNIST dataset: A swap test approach

In this study, we propose a novel modularized Quantum Neural Network (mQNN) model tailored to address the binary classification problem on the MNIST dataset. The mQNN organizes input information using ...
IEEE

Binary Neural Networks With Feature Information Retention for Efficient Image Classification

Abstract: Although binary neural networks (BNNs) enjoy extreme compression ratios, there are significant accuracy gap compared with full-precision models. Previous works propose various strategies to ...