Key Differences Between Neural Networks and Traditional Algorithms

1. Structure:

   • Neural networks are made up of layers of connected nodes, which are also called neurons.
   • Traditional algorithms usually follow set rules and structured paths.

2. Data Handling:

   • Neural networks work best with large amounts of data; they need at least 10,000 data points to get started.
   • Traditional algorithms can do well with smaller amounts of data, often needing less than 1,000 data points.

3. Flexibility:

   • Neural networks can learn complicated patterns and handle various shapes of data.
   • Traditional algorithms usually stick to fixed functions and certain ideas about how things should work.

4. Performance:

   • For tasks like recognizing images, neural networks often reach accuracy levels above 90%.
   • In comparison, traditional methods usually don’t do as well, often scoring below 70%.