CS5720 - Week 3
Slide 60 of 60

Week 3 Summary & Week 4 Preview

Week 3: What We Learned

  • 🏗️ Deep Learning Fundamentals
    What makes networks "deep" and the universal approximation theorem
  • 📉 Gradient Problems
    Vanishing and exploding gradients - causes and solutions
  • 🎲 Weight Initialization
    Xavier/Glorot and He initialization strategies
  • ⚡ Advanced Optimizers
    Beyond SGD - Momentum, Adam, and learning rate scheduling
  • 📊 Model Evaluation
    Metrics, confusion matrices, and practical training tips

Week 4: CNNs - Part 1

  • 👁️ Computer Vision Basics
    Images as data, pixels, tensors, and visual processing
  • 🔍 Convolution Operation
    Filters, kernels, feature detection, and convolution math
  • 🧱 CNN Architecture
    Convolutional layers, pooling, padding, and stride
  • 🏗️ Building Your First CNN
    Hands-on implementation and practical examples
  • 🎯 CNN Applications
    Image classification, object detection, and real-world uses

Course Progress Tracker

1
Neural Network Basics
✅ Completed
2
Training Fundamentals
✅ Completed
3
Deep Learning
🎯 Current
4
CNNs - Part 1
⏳ Next Week
🎉
Congratulations! You've Mastered Deep Learning Fundamentals!
You now understand optimization, evaluation, and practical training techniques. Ready for computer vision!
← Previous Week 4 →
Prepared by Dr. Gorkem Kar