Leadership

Nigerian Researcher Develops Deep Learning Model To Transform Brain Cancer Diagnosis

In a groundbreaking development at the intersection of artificial intelligence (AI) and medicine, Tobi Titus Oyekanmi, a ...
Analytics India Magazine

10 Best AI Courses in 2025: Programs That Bridge Theory and Real-World Application

This course takes a classic, academic approach, offering a deep dive into AI algorithms, logic, probabilistic models, and ...
Frontiers

Effective methods and framework for energy-based local learning of deep neural networks

From a neuroscience perspective, artificial neural networks are regarded as abstract models of biological neurons, yet they rely on biologically implausible backpropagation for training. Energy-based ...
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 ...
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 ...
Hosted on MSN

Deep Neural Network From Scratch in Python ¦ Fully Connected Feedforward Neural Network

Create a fully connected feedforward neural network from the ground up with Python — unlock the power of deep learning! 58 shot, 8 dead, in Chicago amid Trump's threat to deploy National Guard Donald ...
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 ...
GitHub

Hands-On Graph Neural Networks Using Python

What is this book about? Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation ...
C&EN

DeepEpiIL13: Deep Learning for Rapid and Accurate Prediction of IL-13-Inducing Epitopes Using Pretrained Language Models and Multiwindow Convolutional Neural Networks

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Accurate prediction of interleukin-13 (IL-13)-inducing epitopes is crucial for ...
GitHub

bsklzsc/1Flexible-Job-Shop-Scheduling-Via-Graph-Neural-Network-and-Deep-Reinforcement-Learning

Implementation of the IEEE TII paper Flexible Job Shop Scheduling via Graph Neural Network and Deep Reinforcement Learning. IEEE Transactions on Industrial Informatics, 2022. @ARTICLE{9826438, author= ...