Python Status: Pending Migration

📖 Deep Learning¶

  • 🧠 What is Deep Learning?
    • 🔑 Relationship to Neural Networks
    • 🧱 Depth vs. Width
    • 🧠 Representation Learning
  • 🧪 Setup: Complex Dataset
    • 🧬 Example: CIFAR-10 or Similar
    • 📊 Class Imbalance / Real-world Noise
    • 🧮 Feature Complexity vs. Model Depth
  • 🧱 Deep Network Architecture
    • 🏗️ Stacking Layers: Concept and Challenges
    • 🔥 Activation Functions
    • 🧠 Role of Depth in Feature Hierarchy
  • 🎯 Loss Surfaces and Optimization
    • 🌄 Non-convex Landscapes
    • 🌀 Vanishing/Exploding Gradients
    • ⚙️ Weight Initialization Strategies
  • 🧰 Training Deep Networks
    • 🧮 Batch Training and Mini-Batch SGD
    • 🛠️ Gradient Clipping
    • 🚀 Optimizers
  • 🧠 Advanced Training Tricks
    • ⏱️ Learning Rate Scheduling
    • 🧊 Early Stopping
    • 🎲 Dropout in Deep Models
    • 🧪 Data Augmentation
  • 📚 Transfer Learning & Pretraining
    • 🔄 Why Pretrained Models Work
    • 🏗️ Fine-tuning vs. Feature Extraction
    • 🌍 Common Pretrained Networks
  • 📈 Scaling Up
    • 🧮 Depth vs. Performance Tradeoffs
    • 🧠 Hardware Considerations
    • ⚖️ Batch Norm vs. Gradient Flow
  • 🔚 Closing Notes
    • 🧠 Summary of Key Concepts
    • ⚠️ Common Pitfalls in Deep Learning
    • 🚀 What's Next: Transformers & Attention

🧠 What is Deep Learning?¶

🔑 Relationship to Neural Networks¶

🧱 Depth vs. Width¶

🧠 Representation Learning¶

Back to the top

🧪 Setup: Complex Dataset¶

🧬 Example: CIFAR-10 or Similar¶

📊 Class Imbalance / Real-world Noise¶

🧮 Feature Complexity vs. Model Depth¶

Back to the top

🧱 Deep Network Architecture¶

🏗️ Stacking Layers: Concept and Challenges¶

🔥 Activation Functions¶

🧠 Role of Depth in Feature Hierarchy¶

Back to the top

🎯 Loss Surfaces and Optimization¶

🌄 Non-convex Landscapes¶

🌀 Vanishing/Exploding Gradients¶

⚙️ Weight Initialization Strategies¶

Back to the top

🧰 Training Deep Networks¶

🧮 Batch Training and Mini-Batch SGD¶

🛠️ Gradient Clipping¶

🚀 Optimizers¶

Back to the top

🧠 Advanced Training Tricks¶

⏱️ Learning Rate Scheduling¶

🧊 Early Stopping¶

🎲 Dropout in Deep Models¶

🧪 Data Augmentation¶

Back to the top

📚 Transfer Learning & Pretraining¶

🔄 Why Pretrained Models Work¶

🏗️ Fine-tuning vs. Feature Extraction¶

🌍 Common Pretrained Networks¶

Back to the top

📈 Scaling Up¶

🧮 Depth vs. Performance Tradeoffs¶

🧠 Hardware Considerations¶

⚖️ Batch Norm vs. Gradient Flow¶

Back to the top

🔚 Closing Notes¶

🧠 Summary of Key Concepts¶

⚠️ Common Pitfalls in Deep Learning¶

🚀 What's Next: Transformers & Attention¶

Back to the top