## 🔹 Best Practices for CNN Development
1. Start with pretrained models when possible
2. Use progressive resizing (start with small images, then increase)
3. Monitor class activation maps to debug model focus areas
4. Apply test-time augmentation (TTA) for better inference
5. Use label smoothing for classification tasks
6. Implement learning rate warmup for large batch training

# Label smoothing example
criterion = nn.CrossEntropyLoss(label_smoothing=0.1)

# Learning rate warmup
def warmup_lr(epoch, warmup_epochs=5, base_lr=0.001):
    return base_lr * (epoch + 1) / warmup_epochs if epoch < warmup_epochs else base_lr

---

### 📌 What's Next?
In Part 4, we'll cover:
➡️ Recurrent Neural Networks (RNNs/LSTMs)
➡️ Sequence Modeling
➡️ Attention Mechanisms
➡️ Transformer Architectures

#PyTorch #DeepLearning #ComputerVision 🚀

Practice Exercises:
1. Modify the CNN to use depthwise separable convolutions
2. Implement a ResNet-18 from scratch
3. Apply Grad-CAM to visualize model decisions
4. Train on CIFAR-100 with CutMix augmentation
5. Compare Adam vs. SGD with momentum performance

# Depthwise separable convolution example
class DepthwiseSeparableConv(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()
        self.depthwise = nn.Conv2d(in_channels, in_channels, kernel_size=3,
                                  stride=stride, padding=1, groups=in_channels)
        self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        
    def forward(self, x):
        return self.pointwise(self.depthwise(x))

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🌟 Vision Transformer (ViT) Tutorial – Part 4: Beyond Classification – DETR, Segmentation & Video Transformers

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🌟 Vision Transformer (ViT) Tutorial – Part 5: Efficient Vision Transformers – MobileViT, TinyViT & Edge Deployment

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🌟 Vision Transformer (ViT) Tutorial – Part 6: Vision Transformers in Production – MLOps, Monitoring & CI/CD

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🌟 Vision Transformer (ViT) Tutorial – Part 7: The Future of Vision Transformers – Multimodal, 3D, and Beyond

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