How does Neural Network learn?

While neural networks do contain learned information, describing them plainly as storing “compressed knowledge” isn’t quite accurate.

Neural networks store patterns of weights and biases that were optimized during training. These parameters allow the network to recognize patterns and make predictions, but they don’t store knowledge in a way that’s analogous to human memory or a traditional database.

Think of it more like a complex mathematical function that’s been tuned to transform inputs into desired outputs. The “knowledge” isn’t stored in an easily interpretable or compressed format - it’s distributed across billions of parameters in a way that often aren’t straightforward to analyze or understand.

This is why neural networks can sometimes: 

• Make confident predictions that are completely wrong

• Fail to generalize in expected ways

• Have difficulty transferring knowledge to new contexts

• Produce inconsistent outputs.

How does neural network actually store and process information

Consider this as the basic neuron operation:

Where y = output, f is activation function (ReLU, SoftMax, sigmoid etc.) x = input, w = weights, b = bias Network structure:

• Instead of having a clear “knowledge database,” a neural network consists of layers of interconnected artificial neurons

• Each connection has a weight, and each neuron has a bias value

• These weights and biases are what get adjusted during training

Learning Process:

• The network doesn’t memorize facts - it learns to recognize patterns through repeated exposure to training data.

• During training, it adjusts those weights and biases to minimize prediction errors.

• This is more like learning to recognize patterns than storing explicit knowledge.

What’s actually stored:

• Primarily mathematical parameters (weights and biases)

• These parameters define how input signals should be transformed

• The “knowledge” is implicit in these transformations, not explicitly stored

Key Differences from Human Knowledge:

• No semantic understanding

• No concept of causation

• No ability to reason about stored information

• Can’t explain its own decision making process

• Can’t easily transfer learning to new contexts

Example: when a neural network learns to recognize a cat, it’s not storing “cats have fur, four legs, etc.” Instead it’s storing parameters that transform pixel values through mathematical operations that happen to output “cat” when shown cat images.

Think of it less like a library of knowledge and more like a complex filter that’s been shaped by training data to transform inputs into useful outputs.