Scientific Research Publishing

Intelligent Agents in Cybersecurity: Deep Learning to Analyze User Behavior Applying ()

The research aim is to develop an intelligent agent for cybersecurity systems capable of detecting abnormal user behavior ...
Deep Learning with Yacine on MSNOpinion

What Are Tensors in Deep Learning? A Beginner-Friendly Explanation

Tensors are the fundamental building blocks in deep learning and neural networks. But what exactly are tensors, and why are ...

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IIT Delhi’s 6-month programme offers faculty-led, hands-on training in Data Science, ML, Deep Learning and Generative AI, ending with a valuable certificate.
Analytics Insight

10 Best Python Libraries for Computer Vision in 2025

Overview: Python dominates computer vision with its vast array of open-source libraries and active community support.These ...
SecurityWeek

Data Exposure Vulnerability Found in Deep Learning Tool Keras

A vulnerability in the open source deep learning tool Keras could allow attackers to load arbitrary local files or conduct ...
Phys.org

Interpretable deep learning network significantly improves tropical cyclone intensity forecast accuracy

Predicting tropical cyclones (TCs) accurately is crucial for disaster mitigation and public safety. Although the forecasting accuracy of TC tracks has improved substantially in recent decades, ...
Hosted on MSN

Dropout In Neural Networks — Prevent Overfitting Like A Pro (With Python)

This video is an overall package to understand Dropout in Neural Network and then implement it in Python from scratch. Dropout in Neural Network is a regularization technique in Deep Learning to ...
Frontiers

Editorial: Deep neural network architectures and reservoir computing

Over the past decade, deep learning (DL) techniques such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks have played a pivotal role in advancing the field of ...
EurekAlert!

Brain cells learn faster than machine learning, new research reveals

Melbourne, Australia - 12 August 2025 - Researchers have demonstrated that brain cells learn faster and carry out complex networking more effectively than machine learning by comparing how both a ...
IEEE

Energy-efficient Distributed Heterogeneous Hybrid Flow-shop Scheduling Using Graph Neural Network and Deep Reinforcement Learning

Abstract: With growing environmental awareness and increasing energy demands, sustainable manufacturing has become a focal point in the industry. Meanwhile, globalization has propelled distributed ...
Scientific Research Publishing

Hybrid PINN-LSTM Model for River Temperature Prediction: A Physics-Informed Deep Learning Approach ()

This study proposes a hybrid modeling approach that integrates a Physics Informed Neural Network (PINN) and a long short-term memory (LSTM) network to predict river water temperature in a defined ...
IEEE

Learning laws for deep convolutional neural networks with guaranteed convergence

Abstract: Convolutional Neural Networks (CNNs) have shown remarkable success across numerous tasks such as image classification, yet the theoretical understanding of their convergence remains ...