Overfitting Explained, Best Tricks to Make Your AI Smarter in Skin Cancer Detection

Is your neural network performing well in training but failing in real-world data? That’s Overfitting! 🤯

We faced this challenge while building an AI model to detect Skin Cancer from medical images—and in this video, we’ll show you exactly how we fixed it!

What You’ll Learn in This Video:
✅ What is Overfitting? Why does it happen in AI models?
✅ How to Detect Overfitting? Warning signs in your training logs!
✅ Best Fixes for Overfitting:

Dropout Layers (Randomly disable neurons to improve generalization)
L2 Regularization (Keep weights small & prevent overconfidence)
Data Augmentation (Flip, rotate, and modify images to improve learning)
Early Stopping (Stop training at the right time to avoid memorization)
Reducing Model Complexity (Avoid too many unnecessary parameters)
📊 Real-world Example: We applied these techniques to train a Convolutional Neural Network (CNN) for skin cancer detection using ISIC medical image data. By the end of this video, you’ll know exactly how to improve your neural networks and make them generalize better on real-world medical images! 🚀

Resources:

Google Colab : https://colab.research.google.com/drive/1THKliOKXQcOu41oT6sJmQXahQJXFoKer?usp=sharing

Github repo: https://github.com/sgdataguru/28DaysChallenge

Discord Server: https://discord.gg/GvN8pFzF

28 Challenge Playlist : https://www.youtube.com/watch?v=Tuinz05fsDU&list=PLOunRutLyZBImuDC24ijOdl5AqBnlRhaJ

LinkedIn: https://www.linkedin.com/in/maheshkumarpaik/

Telegram Link: https://t.me/maheshpaik

Instagram: https://www.instagram.com/maheshpaikk

Are there errors in any of the codes that I have explained? Mail the details of the error to maheshkumar.paik@outlook.com

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