Deep Learning & Neural Networks
Visualizing how layers of artificial neurons process data to create intelligence.
GPT-4 Parameters
~1.8 Trillion
Human Neurons
~86 Billion
Training Data
Trillions of words
Training Time
Weeks on 1000s of GPUs
Deep Learning is a specialized field of machine learning that uses 'Artificial Neural Networks' to solve complex problems. These networks are loosely inspired by the biological neurons in our brains. While standard machine learning might have 1 or 2 layers of processing, 'Deep' learning uses hundreds or even thousands of layers. Each layer extracts more complex features from the data. For example, in image recognition, the first layer might find edges, the second layer finds shapes (like circles or squares), and the final layers identify complex objects like 'human faces' or 'bicycles crystalline.'
This 'depth' is what allows AI to understand context. In traditional ML, engineers had to tell the computer which features were important (like 'look for wheels to find a car'). In Deep Learning, the network discovers these features automatically. This is called 'feature extraction.' This breakthrough is what made voice assistants like Siri and Alexa possible, as they must process the nuances of human speech, which is incredibly complex and varies by accent and emotion.
The Power of GPUs
Deep Learning was theoretical for decades. It only became practical recently because of Graphics Processing Units (GPUs). Originally made for video games, GPUs are very good at performing the billions of simple math operations needed to train a deep neural network. Without gaming hardware, we wouldn't have modern AI!