Daily Excelsior

Machine Learning Methods Used for Portfolio Optimization and Risk Management

Machine learning is reshaping the way portfolios are built, monitored, and adjusted. Investors are no longer limited to ...
Deep Learning with Yacine on MSN

How to implement stochastic gradient descent with momentum in Python

Learn how to implement SGD with momentum from scratch in Python—boost your optimization skills for deep learning.
IEEE

Enhancing Hyperspectral Image Analysis With Deep Learning: An Innovative Hybrid Approach for Improved Detection and Classification Using Deep-Detect

Abstract: Surface material identification using hyperspectral imaging (HSI) analysis is a crucial and challenging issue in remote sensing. Researchers widely recognize that exploiting spectral-spatial ...
Ministry of Testing

Back to the basics: Rethinking how we use AI in testing

A simple rule of thumb: In general, AI is best reserved for well-defined, repetitive tasks. This includes anything that ...
eLife

Separating selection from mutation in antibody language models

This important study introduces a new biology-informed strategy for deep learning models aiming to predict mutational effects in antibody sequences. It provides solid evidence that separating ...
TMCnet

Bridging Silence: A Real-Time Sign Language to English Text Translation System Using Python, OpenCV, and Convolutional Neural Networks

Bridging communication gaps between hearing and hearing-impaired individuals is an important challenge in assistive ...
Learn With Jay on MSN

Build a deep neural network from scratch in Python

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write ...
GitHub

TabM: Advancing Tabular Deep Learning With Parameter-Efficient Ensembling" (ICLR 2025)

This is the official repository of the paper "TabM: Advancing Tabular Deep Learning With Parameter-Efficient Ensembling". It consists of two parts: One dot represents a performance score on one ...
IEEE

Phishing Website Detection Using Deep Learning Models

Abstract: This research addresses the imperative need for advanced detection mechanisms for the identification of phishing websites. For this purpose, we explore state-of-the-art machine learning, ...