Description
Modern Deep Convolutional Neural Networks with PyTorch
Go beyond basic CNNs and implement **cutting-edge architectures** used in research and industry—ResNet, DenseNet, Inception, EfficientNet, and more—all in **PyTorch**, the framework of choice for AI researchers at Meta, Tesla, and OpenAI.
What You’ll Build
- A custom CNN from scratch—no torchvision.models shortcuts
- A ResNet-18 implementation with skip connections and batch norm
- An image classifier using transfer learning and fine-tuning
- A neural style transfer system using VGG features
- A medical image segmenter using U-Net architecture
Key Concepts You’ll Master
- Modern CNN blocks—bottleneck layers, depthwise separable convs
- Advanced regularization—DropBlock, Stochastic Depth, Mixup
- Training tricks—Label smoothing, cosine annealing, learning rate warmup
- Efficient architectures—MobileNet, EfficientNet for edge deployment
- Debugging & visualization—CAM, Grad-CAM, feature maps
Why PyTorch for CNNs?
- Research standard—90%+ of CVPR papers use PyTorch
- Dynamic graphs—easier to debug and modify than static graphs
- TorchVision & TorchMetrics—batteries included for CV tasks
Who Should Take This?
- Intermediate PyTorch users moving beyond tutorials
- Computer vision engineers preparing for technical interviews
- Graduate students implementing papers
- Competitive Kagglers optimizing image models
Your Path to SOTA Vision Models Starts Here
This course skips toy MNIST examples and dives into **real-world, production-grade CNN engineering**—exactly what top AI teams expect.
Ready to build what’s next in computer vision? Enroll now.
