All-in-one Activation Functions | Sigmoid, tanh, ReLU, Leaky ReLU, ELU, Swish | Deep Learning basics

🌟 In this video, we dive deep into some of the popular activation functions, exploring their unique properties and how they impact our neural network models. Get ready to level up your understanding of these crucial elements in deep learning! 📈
🎬 Here's what we cover:
Step Function: 🚶‍♂️ Simple yet powerful, we examine its binary nature and its implications for neural networks.
Sigmoid: 🌈 The smoothness and probability like output of the Sigmoid function made it popular and its role in introducing non-linearity to our models was a fresh change.
Tanh: 🌀 Explore the hyperbolic tangent function and its advantages over the sigmoid, including zero-centeredness.
ReLU (Rectified Linear Unit): ⚡️ Meet the superstar of activation functions! We discuss its simplicity, non-saturation, and computational efficiency.
Leaky ReLU: 💧 Dive into the Leaky ReLU, addressing the "dying ReLU" problem and its implications for model training.
ELU (Exponential Linear Unit): 🌟 Uncover the ELU's unique features, including smoothness and negative input handling.
Swish: 🎉 Last but not least, we introduce the Swish function, hailed for its self-gating mechanism and improved performance.
🔍 Throughout the video, we evaluate each function based on critical properties:
Non-Linearity: 📈 We assess how each function introduces non-linearity into our network, crucial for modeling complex relationships.
Differentiability: 📝 We examine the differentiability of each function and its implications for gradient-based optimization.
Zero-Centeredness: 🎯 Learn why having activation functions centered around zero can aid in faster convergence during training.
Computational Efficiency: ⏱️ Explore the computational costs associated with each function and how they impact model training and inference.
Saturation Effects: 💥 Uncover the phenomena of saturation and its consequences on gradient flow and model performance.
🔑 By the end of this video, you'll have a comprehensive understanding of each activation function's strengths and weaknesses, empowering you to make informed decisions when designing your neural networks! 💡 Don't miss out on this exciting journey through the heart of deep learning! 🚀

[@trustoriakhi5786]

Thank you so much for your content

[@ai-touch9]

Well explained, Thanks for your work

[@prosmartanalytics]

Glad it was helpful!😊