The intuition behind neural networks and why they need activation functions. The post Neural Networks, Explained for Beginners: Start Here If They’ve Confused You 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
They aren’t designed, you can’t help perceiving one anyway, and that makes them an engineering problem almost no one is solving. The post Where Does an AI’s Personality Actually Come From?
By Slava Polonski, PhD
A step-by-step journey from calculus-based optimization to Stochastic Gradient Descent The post Why Gradient Descent Became Stochastic appeared first on Towards Data Science .
By Nikhil Dasari
The article "Diversity of EML-type operators" discusses the EML operator, which can evaluate standard explicit purely transcendental elementary functions, and notes that while most research has focused on the binary EML, many similar variants exist. It enumerates and classifies these variants, clarifies common misconceptions, and proposes a M"obius layer that replaces matrix operations with rational functions. The paper also showcases the activation function eml(x,1/x), enabling separate recovery of exp(x) and ln(x) and thus evaluation of all elementary functions within a rational neural‑network generalization.
By Andrzej Odrzywo{\l}ek
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