Activation Functions Explained: Why ReLU Replaced Sigmoid

📰 Medium · AI

Learn why ReLU replaced Sigmoid as the primary activation function in neural networks and how to apply this knowledge to improve your own models

intermediate Published 13 Apr 2026
Action Steps
  1. Read about the limitations of Sigmoid activation functions
  2. Understand the benefits of ReLU activation functions
  3. Apply ReLU to your own neural network models
  4. Compare the performance of Sigmoid and ReLU in different scenarios
  5. Experiment with other activation functions to find the best fit for your specific use case
Who Needs to Know This

Data scientists and machine learning engineers can benefit from understanding the role of activation functions in neural networks to design and optimize more effective models

Key Insight

💡 ReLU activation functions have largely replaced Sigmoid due to their ability to avoid vanishing gradients and improve model performance

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🤖 Did you know why ReLU replaced Sigmoid as the primary activation function in neural networks? 📈 Learn more about the benefits and limitations of each! #MachineLearning #NeuralNetworks

Key Takeaways

Learn why ReLU replaced Sigmoid as the primary activation function in neural networks and how to apply this knowledge to improve your own models

Full Article

Title: Activation Functions Explained: Why ReLU Replaced Sigmoid

URL Source: https://medium.com/@adityaa9971/activation-functions-explained-why-relu-replaced-sigmoid-26242bacdb6b?source=rss------artificial_intelligence-5

Published Time: 2026-04-13T03:16:19Z

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# Activation Functions Explained: Why ReLU Replaced Sigmoid | by ADITYA RANJAN | Apr, 2026 | Medium

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# Activation Functions Explained: Why ReLU Replaced Sigmoid

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The function inside every neuron that makes neural networks actually work and why choosing the wrong one breaks everything.

### about me

Aditya Ranjan

3rd-year ECE student at GGSIPU, Delhi. Self-learning Gen AI and full-stack development, and writing everything down so beginners don’t have to struggle the way I did. This is Blog #5 of the Gen AI Journey series if you missed Blog #4 on Neural Networks, read that first.

## Introduction

In Blog #4, we learned that every neur
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