Neural networks are computer systems modeled after the interconnected cells in a human brain. They learn to recognize patterns by passing signals through a complex web of parts.

Brain-Inspired Design

This is the basic building block of the network, acting like a tiny decision-maker. It receives signals, processes them, and decides whether to send information to the next unit.

The Artificial Neuron

This is the first layer where raw information, like pixels from a photo, enters the network. It acts like the system's eyes, taking in data for the rest of the layers to see.

The Input Layer

Often called hidden layers, these middle steps look for patterns that aren't obvious. They handle the complex thinking required to understand the data.

The Processing Layers

This is the last layer that provides the final answer or prediction. It might tell you the name of an object in a picture or the next word in a sentence.

The Final Output

These act like volume knobs that make some signals stronger and others weaker. They help the network prioritize the most important pieces of information.

Importance Weights

This is a base value that helps a neuron decide when it's time to send a signal. It ensures the network can make correct decisions even when data is slightly different.

The Bias Setting

This is a mathematical rule that acts like a gatekeeper. It determines if the combined signals are strong enough to 'fire' and move to the next layer.

The Activation Switch

Neurons are organized in layers, and signals typically flow from one layer to the next. In most networks, every neuron in one layer is linked to every neuron in the following layer.

Layer-to-Layer Connections

This is the one-way journey information takes from the input to the output. As data moves forward, it becomes more refined until a final decision is made.

Forward Signal Path

The network improves by looking at how far its guess was from the correct answer. It then works backward to slightly adjust its internal settings to be more accurate next time.

Learning From Mistakes

When a network has many layers stacked together, it is called a 'deep' network. Having more layers allows the computer to understand much more complex and detailed ideas.

What are Importance Weights?