Confused about activation functions in neural networks? This video breaks down what they are, why they matter, and the most common types — including ReLU, Sigmoid, Tanh, and more! #NeuralNetworks ...

Abstract: The radial basis function neural network (RBFNN) is a learning model with better generalization ability, which attracts much attention in nonlinear system identification. Compared with the ...

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

In this work, we introduce a new method ability radial basic function-partial least square (RBF-PLS) with high accuracy and precision in QSPR studies. Three quantitative structure-propertty ...

Cucumber cultivation faces two pressing challenges: balancing shoot architecture with drought tolerance. New research has uncovered that the CsTIE1–CsAGL16 module serves as a pivotal regulator in ...

Hi, thank you for this great package! I was wondering whether it would be straightforward to combine this package with Lux.jl to build a radial basis function network (RBFN). Has anyone tried that?

Recent work has established an alternative to traditional multi-layer perceptron neural networks in the form of Kolmogorov-Arnold Networks (KAN). The general KAN framework uses learnable activation ...

1 School of Mechanical and Electronic Engineering, Hubei Polytechnic University, Huangshi, China 2 Hubei Key Laboratory of Intelligent Conveying Technology and Device, Hubei Polytechnic University, ...