CMSC 191: Introduction to Neural Computing
Multilayer Networks and Backpropagation
Embedded Files
From Linear Models to Deep Learning
From Linear Models to Deep Learning
In this topic, we’ll explore the breakthrough that took neural computing from simple linear models to powerful systems capable of learning complex, nonlinear patterns. You’ll discover how multilayer feedforward networks solved the limitations of the Perceptron by introducing hidden layers and non-linear activation functions, opening the door to far more sophisticated learning.
We’ll dive into how these hidden layers help neural networks build hierarchical representations—where each layer adds more complexity and abstraction, allowing the network to recognize patterns at different levels of detail. As we move forward, we’ll break down backpropagation, the algorithm that uses the chain rule of calculus to efficiently assign credit and spread errors across layers. This algorithm is a key innovation that allows neural networks to learn and improve in a smart, systematic way.
Taken together, these ideas set the stage for the rise of deep learning, a paradigm that reshaped the field of artificial intelligence and continues to power many of the most exciting advances in technology today.
Learning Outcomes
Learning Outcomes
Describe the architecture and role of hidden layers in feedforward neural networks.
Explain how non-linear activation functions enable multilayer networks to model complex patterns.
Interpret the Universal Approximation Theorem as a theoretical foundation for function approximation.
Explain the backpropagation algorithm as an efficient solution to the credit assignment problem.
Discuss the historical impact of backpropagation in reviving and advancing neural computing research.
Guide Questions
Guide Questions
Why does adding hidden layers transform the learning capacity of neural networks?
How does backpropagation efficiently distribute learning across layers using the chain rule?
What made the rediscovery of backpropagation a turning point in the history of AI?
Topic Outline
Topic Outline
Multilayer Networks and Backpropagation* (topic handout)
When Depth Became Intelligence
Architecture of Feedforward Neural Networks
Solving Non-linearity with Hidden Layers
Building Abstractions: The Power of Representation
Backpropagation Algorithm
Credit Assignment and the Chain Rule
Reigniting the Field: The Modern Dawn of Deep Learning
The Algorithm That Sparked a Revolution
The semester at a glance:
Multilayer Networks . . .
Depth gave networks vision; backpropagation gave them understanding.
Depth gave networks vision; backpropagation gave them understanding.
Page updated
Report abuse