Let's discover how neural networks learn, step by step The post Backpropagation Explained for Beginners (Part 1): Building the Intuition appeared first on Towards Data Science .
By Nikhil Dasari
From one gradient to every gradient The post Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works appeared first on Towards Data Science .
By Nikhil Dasari
arXiv:2507. 14177v2 Announce Type: replace-cross Abstract: This paper aims to understand the training solution, which is obtained by the back-propagation algorithm, of two-layer neural networks whose hidden layer is composed of the units with smooth activation functions, including the usual sigmoid type most commonly used before the advent of ReLUs.
By Changcun Huang
arXiv:2006. 04013v6 Announce Type: cross Abstract: Artificial Intelligence (AI) has been adopted in a wide range of domains.
By Rubens Lacerda Queiroz, F\'abio Ferrentini Sampaio, Cabral Lima, Priscila Machado Vieira Lima
The article titled "The Sigmoid Function: From 'e' to Neural Networks" explores the origins and applications of the sigmoid function, a mathematical equation frequently used in data science and machine learning. It traces the function’s development from its foundational exponential form to its modern role in neural network architectures. The piece highlights how this simple yet powerful equation underpins many computational models in the field.
By Nikhil Dasari