🎉 ResNet Tutorial: Understanding Convolutional Neural Networks

What is ResNet?

ResNet (Residual Network) is a deep learning architecture that solves the vanishing gradient problem through residual blocks. Introduced by Kaiming He et al. in 2015, it revolutionized image recognition tasks by enabling the training of networks with hundreds of layers.

Key Features of ResNet

• ✅ Skip Connections: Connects input to output across layers, allowing gradients to flow directly
• 🧠 Deep Networks: Supports architectures like ResNet-50, ResNet-101, and ResNet-152
• 📈 High Accuracy: Achieves state-of-the-art performance on benchmarks like ImageNet

Architecture Overview

The core of ResNet is the residual block, which contains:

1. Convolutional layers with kernel size 3x3
2. Batch normalization
3. ReLU activation
4. Down-sampling layers (optional)
5. Identity mapping for skip connections

Training Tips

• 🔍 Use pre-trained models for faster convergence
• 🔄 Implement stochastic depth for regularization
• 📊 Monitor loss curves to detect vanishing gradients

Further Reading

For a deeper dive into CNN fundamentals, check our Convolutional Neural Networks Overview tutorial. You can also explore ResNet Implementation Details for code examples.