SUMMARY.md

Summary

Note that these are the subsections I was able to come up with on the spot. For the topics you are working on, please elaborate/slash some of the topics. You are free to do whatever you want.

When compiling, formating of this file is important.

• Getting Started
  • Welcome
  • Installation & Set-up
• The Libraries
• The Math You Need
• Deep Learning
  • What is Deep Learning
  • Types of Learning
  • Core Concepts of Deep Learning
    • Datasets
    • Neural Network
    • Gradient Descent
    • Learning Rates
    • Loss Functions
    • Metrics
    • Code It Up from Scratch
• CNN
  • Why Not MLPs
  • What are CNNs
  • Why Do CNNs Work
  • Revolutionary CNNs
    • Vanishing Gradient? Introduce Skip Connections
  • Limitations
• RNNs
• Reinforcement Learning
• Transformers
  • Motivations for Transformers
  • Transformers vs. Previous Models
  • The Structure
    • Attention Mechanism and Why
    • Self-Attention and Multi-Head Attention
    • Positional Encoding
    • Encoder and Decoder Structures
    • Feed-Forward Layers
    • Residual Connections and Layer Normalization
  • Popular Transformer Models
• Units to Take