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Over the past few years, generative models like GANs and VAEs have dominated the AI scene. But recently, diffusion models have stepped into the spotlight—powering cutting-edge tools like DALL-E 2, Midjourney, and Stable Diffusion. So what makes them so powerful, and why are they taking over? What Are Diffusion Models? Diffusion models are a class of generative models that learn to create data by reversing a noising process. In training, they gradually corrupt data with noise; in generation, they learn to reverse this process to produce realistic outputs from pure noise. Conceptually, it is like teaching a model how to clean up an image that has been buried under layers of static. Why Are They Better Than GANs? Unlike GANs, which often suffer from mode collapse and training instability, diffusio...

When training deep neural networks, choosing the right activation function can make a huge difference. One of the most common choices— ReLU (Rectified Linear Unit) —is popular for its simplicity and speed. But it's not without flaws. Enter: Leaky ReLU — a small tweak that solves a big problem. The Problem with Vanilla ReLU ReLU is defined as: f(x) = max(0, x) Which means: If x > 0 , output is x If x , output is 0 While this works great most of the time, it comes with a major drawback : Dead Neurons Problem If a neuron gets stuck in the negative range, it always outputs 0. That means no gradient, no learning — it's "dead." Over time, a portion of your network can become inactive, especially in deep architectures. How Leaky ReLU Fixes This Leaky ReLU modifies the function like so: f(x) = x if x > 0 f(x) = αx if x This tiny slope for negative values (instead of zero) helps keep the neuron alive and gradients flowing...