ReLU and Leaky ReLU Activation Functions in Deep Learning
Activation functions are at the core of what makes neural networks capable of learning complex patterns in data. But what exactly is an activation function, and why is it so important? 🤔
In this video, we explore the role of activation functions in neural networks, helping you understand why they are crucial for adding non-linearity and enabling models to handle complex data like images, speech, and text.
Topics Covered in the Video:
ReLU Activation Function:
ReLU is the most widely used activation function today, known for its simplicity and computational efficiency. We discuss how ReLU works, its formula, and where it’s used in modern deep learning architectures.
However, ReLU isn't perfect. It can suffer from the Dying ReLU problem, which can cause neurons to stop learning. We discuss why this happens and the potential solutions, like reducing the learning rate.
Leaky ReLU Activation Function and PReLU:
A powerful alternative to ReLU is Leaky ReLU, which introduces a small negative slope for negative inputs, preventing neurons from "dying" and keeping them active throughout training. We discuss how the formula works and why Leaky ReLU can be a better option in some cases.
The following video contains coding ReLU and Leaky ReLU Activation Functions using Pytorch:
• Code: ReLU and Leaky ReLU in Deep Lea...
Thank you!
Dr. Shahriar Hossain
https://computing4all.com