CS5720 - Week 4
Slide 80 of 80

Week 4 Summary & Week 5 Preview

Week 4 Key Concepts

  • 👁️
    Computer Vision Foundations
    Images as data, pixels as tensors, spatial structure
  • 🔍
    Convolution Operation
    Feature detection, filters, kernels, stride & padding
  • 📉
    Pooling Operations
    Downsampling, max/average pooling, dimension reduction
  • 🔗
    Parameter Sharing
    Weight sharing, memory efficiency, translation invariance
  • 🏗️
    CNN Architecture
    Conv → ReLU → Pool → FC layers, building blocks
  • ⚡
    CNN Advantages
    Efficiency, spatial awareness, better performance than FC

Week 5 Preview

  • 🏛️
    Famous CNN Architectures
    LeNet, AlexNet, VGG, ResNet - evolution of CNNs
  • 📈
    Going Deeper
    Skip connections, residual learning, very deep networks
  • 🎯
    Advanced Training
    Data augmentation, batch normalization, optimization
  • 🔄
    Transfer Learning
    Pre-trained models, fine-tuning, feature extraction
  • 👀
    CNN Visualization
    Understanding what CNNs learn, feature maps
  • 💻
    Practical Implementation
    Real projects, image classification, deployment

Your Deep Learning Journey

✅
Weeks 1-3
Neural Network Foundations
→
🎯
Week 4
CNN Fundamentals
→
🚀
Week 5
Advanced CNNs
→
🔮
Weeks 6-7
RNNs & Sequences
🎉 Congratulations! You've Mastered CNN Basics
You now understand the fundamental concepts of Convolutional Neural Networks, including convolution operations, parameter sharing, pooling, and why CNNs dramatically outperform fully connected networks for computer vision tasks.
🔥 Coming Next Week: Advanced CNN Architectures
Discover how researchers pushed CNNs to new heights with innovative architectures like ResNet and VGG. Learn transfer learning techniques that let you leverage pre-trained models for your own projects!
← Previous Next Week →
Prepared by Dr. Gorkem Kar