5. The Emergence of Deep Learning: The Age of Deep Neural Networks

Deep Neural Networks (DNNs): Learning in Depth

2000s: The Breakthrough

Deep Neural Networks, or DNNs, are like supercharged versions of MLPs with many layers. They can learn very complex patterns and relationships by processing data through multiple layers, each learning more intricate features.

How DNNs Work:

1. Multiple Layers: Imagine peeling an onion. Each layer of the network peels back more details, learning from the simple to the complex.
2. Activation Functions: These are like filters that decide what information to pass through each layer, helping the network learn more effectively.

Real-World Applications:

DNNs have led to breakthroughs in image recognition, natural language processing, and even playing complex games like Go.