NN#3 Neural Networks Decoded: Concepts Over Code 0 like February 10, 2025Share this postAuthor(s): RSD Studio.ai Originally published on Towards AI. Beyond the Step: Unveiling Activation Functions The Secret Sauce of Neural Network Non-LinearityThis member-only story is on us. Upgrade to access all of Medium.Source: Youtube/NormalizedNerdIn our previous article, From Linear to Nonlinear: How Stacking Perceptrons Birthed Deep Learning, we took a crucial leap from the limitations of single perceptrons to the power of Multilayer Perceptrons (MLPs). We discovered that simply stacking perceptrons wasnt enough to solve complex problems; the magic ingredient was non-linearity. This realization brought us to the doorstep of the activation function the unassuming component that truly unlocks the potential of neural networks.From Linear to Nonlinear: How Stacking Perceptrons Birthed Deep Learningpub.towardsai.netActivation functions are, in essence, the secret sauce that allows neural networks to learn intricate patterns and model the non-linear relationships that abound in the real world. But what are these functions, and why are they so important? More importantly, where do they come from?In this article, well embark on a journey to explore the diverse world of activation functions. Well uncover their history, understand their purpose, and explore their unique properties, all while keeping our focus firmly on conceptual understanding and intuitive explanations. Get ready to meet the unsung heroes of the neural network revolution!A Quick Note: Just remember the requirement of type of activation function in Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post