Training Deep Networks

Building a neural network is just the first step. The real challenge lies in training it effectively. This module covers the mathematical engines and practical techniques that allow deep models to learn complex patterns.

Chapters

  1. Backpropagation
    • Understand the “Engine of Learning”.
    • Interactive Computational Graph.
    • Implement Backprop from scratch in NumPy.
  2. Optimizers
    • Navigate the loss landscape.
    • Compare SGD, Momentum, and Adam interactively.
    • PyTorch implementation details.
  3. Batch Normalization
    • Solve Internal Covariate Shift.
    • Visualize activation distributions during training.
    • Implement BatchNorm in Python.
  4. Module Review
    • Test your knowledge with Flashcards.
    • Quick Revision Cheat Sheet.