Neural Networks

This module introduces the foundational concepts of Deep Learning, starting from the basic building block, the Perceptron, to complex Multi-Layer Networks.

Module Contents

  1. The Perceptron
    • Understand the history and mechanics of the simplest neural network unit.
    • Explore the Perceptron Learning Algorithm and its limitations.
  2. Activation Functions
    • Dive into the non-linear functions that power neural networks.
    • Compare Sigmoid, Tanh, ReLU, and Softmax.
  3. Multi-Layer Networks
    • Scale up from a single neuron to a deep network.
    • Learn about the Universal Approximation Theorem.
  4. Module Review
    • Review key concepts with flashcards and a cheat sheet.
    • Test your knowledge and prepare for the next module.