CS5720 - Week 2
Slide 40 of 40

Week 2 Summary & Week 3 Preview

📚 What We Learned This Week

Week 2 covered the fundamental concepts of training neural networks, from basic learning principles to practical implementation techniques.
  • 🧠
    The Learning Problem
    Understanding how neural networks learn from data
  • 📊
    Loss Functions
    MSE, cross-entropy, and measuring prediction errors
  • ⛰️
    Gradient Descent
    Batch, stochastic, and mini-batch optimization
  • 🔄
    Backpropagation
    How gradients flow backward through networks
  • 📈
    Overfitting & Underfitting
    Bias-variance tradeoff and generalization
  • ⚖️
    Regularization
    Dropout, weight decay, and early stopping
  • 💻
    Hands-On Training
    Complete workflow from data to trained model

🔮 Week 3 Preview

Week 3 dives into deep learning fundamentals, advanced optimization, and practical techniques for training deeper networks.
  • 🏗️
    What Makes Networks "Deep"?
    Understanding depth and representation learning
  • 💫
    Vanishing/Exploding Gradients
    Common problems in deep networks and solutions
  • 🎯
    Weight Initialization
    Xavier, He, and modern initialization strategies
  • 🧪
    Batch Normalization
    Stabilizing training in deep networks
  • 🚀
    Advanced Optimizers
    Adam, momentum, and adaptive learning rates
  • 🔧
    Hyperparameter Tuning
    Grid search, random search, and best practices

🗺️ Your Deep Learning Journey

Week 1
Neural Network Basics
Perceptrons, MLPs, activations
Week 2
Training Fundamentals
Loss, gradients, regularization
Week 3
Deep Learning
Deep networks, optimization
Weeks 4-5
CNNs
Computer vision, convolutions
🏆 Week 2 Achievements Unlocked!
🎓
Neural Network Trainer
⚡
Gradient Descent Master
🛡️
Overfitting Defender
💻
Hands-On Implementer
📋 Recommended Next Steps
  • 🔬 Practice implementing the training loop in your preferred framework
  • 📊 Experiment with different loss functions and optimizers
  • 🎯 Try training a model on a real dataset (e.g., Iris, Boston Housing)
  • 📚 Review the mathematical concepts if they were challenging
  • 🚀 Prepare for Week 3: Deep Learning Fundamentals!
← Previous Week 3 →
Prepared by Dr. Gorkem Kar