Activation Functions Explained: Sigmoid, ReLU, GELU & SwiGLU Math

Why are activation functions explained like this the key to ChatGPT's brain? Discover why Sigmoid, ReLU, and GELU are the silent engines of modern AI. To understand modern artificial intelligence, we must look at the hidden gatekeepers of neural networks. This deep dive breaks down the mathematical mechanics of non-linear mapping, tracing the evolution from Sigmoid to ReLU and GELU. We explain the vanishing gradient problem and how it halted early deep learning, before showing how the ReLU toggle switch saved deep learning but introduced the risk of permanently dead neurons. Finally, we explore why large language models default to GELU for stable, high-performance training at scale. Which activation function do you use in your models: ReLU, GELU, or SwiGLU? Let us know in the comments! ✦ What is the vanishing gradient problem explained in simple terms? ✦ How does the dying ReLU problem permanently disable neural network pathways? ✦ Why do modern large language models use GELU instead of traditional activation functions? ✦ How does the Gaussian cumulative distribution function enable smooth, probabilistic gating? This video is built on peer-reviewed research, referencing Hendrycks and Gimpel's original 2016 GELUs paper, Devlin et al.'s BERT paper, and Radford et al.'s GPT series. By focusing on step-by-step derivative calculations and worked numerical examples, we fill the gap left by generic tutorials to give you an intuitive yet mathematically rigorous understanding of these critical AI components. #deeplearning #machinelearning #neuralnetworks #artificialintelligence #transformers