Deep learning is a subset of machine learning and artificial intelligence (AI) that uses algorithms called neural networks, inspired by the human brain. These neural networks are made up of layers of nodes (neurons), which process data and identify patterns to make decisions.
Deep learning works best with large datasets and is widely used in tasks like image recognition, speech processing, and language translation. It learns automatically from raw data by passing it through multiple layers of computation.
Simple Example: Recognizing Handwritten Digits
Imagine a deep learning system trained to recognize handwritten numbers (0-9):
- Input Layer:
- You input a scanned image of the number “7”.
- The image is converted into pixel values.
- Hidden Layers:
- These layers process the pixel values.
- The first layer may detect edges, the second identifies shapes, and subsequent layers combine these features to identify the digit.
- Output Layer:
- The system predicts the number and outputs “7”.
Diagram
Here’s a basic diagram of a deep learning neural network:
Input Layer: Hidden Layers: Output Layer:
[ Pixel Data ] → [ Edge Detection ] → [ Shape Detection ] → [ Prediction ]
Or in a layered representation:
[ Input Layer ] → [ Hidden Layer 1 ] → [ Hidden Layer 2 ] → ... → [ Output Layer ]
deep learning neural network:
