Explore the fundamentals of multi-layer perceptrons in this 14-minute educational video from CodeEmporium. Learn how MLPs differ from basic perceptrons through three key distinctions: hidden units, non-linear activation functions, and the backpropagation learning algorithm. The video begins with an introduction to perceptrons, compares them to their multi-layer counterparts, and systematically explains each critical difference that enables MLPs to solve complex problems beyond the capabilities of simple perceptrons. Complete with a quiz section and comprehensive summary, this tutorial provides valuable insights into neural network architecture fundamentals while referencing seminal papers in the field. Access additional resources including interactive demos, academic papers, and related learning materials through the provided links.
Overview
Syllabus
0:00 Introducing the Perceptron
2:22 Perceptron vs Multi-layer perceptron
3:37 Difference 1: Hidden units
5:59 Difference 2: Non-linear units
8:02 Difference 3: New learning algorithm back propagation
11:48 Quiz Time
12:33 Summary
Taught by
CodeEmporium
