How Are Artificial Neural Networks Made?

Artificial neural networks are made of artificial neurons, which are software modules called nodes, working together as software programs or algorithms on computing systems to solve problems through mathematical calculations.

Artificial neural networks (ANNs) are fundamental components of artificial intelligence and machine learning. Unlike biological brains, which are physical structures, ANNs are primarily **software constructs**.

## Building Blocks of ANNs

At their core, artificial neural networks are built from:

*   **Artificial Neurons (Nodes):** These are the basic processing units of an ANN. As stated in the reference, these are **software modules**. Think of them as simple computational units that receive input, process it, and produce an output.
*   **Connections:** These links between neurons carry the output of one neuron as input to another. Each connection has a weight associated with it, which determines the strength and influence of the signal passing through it.
*   **Layers:** Neurons are typically organized into layers:
    *   **Input Layer:** Receives the initial data.
    *   **Hidden Layers:** One or more layers where the bulk of the processing occurs.
    *   **Output Layer:** Produces the final result.

## How They Work Together

The reference highlights that **artificial neural networks are made of artificial neurons that work together to solve a problem**. This collaboration involves:

1.  **Processing Data:** Data enters the input layer.
2.  **Weighted Sums:** Each neuron in a subsequent layer receives inputs from the previous layer. These inputs are multiplied by the weights of their respective connections and summed up.
3.  **Activation Function:** The sum is then passed through an activation function, which introduces non-linearity and determines the neuron's output.
4.  **Propagation:** The output of neurons in one layer becomes the input for neurons in the next layer, propagating the information forward through the network.
5.  **Learning (Training):** The network learns to perform a task (like recognizing images or translating languages) by adjusting the weights of its connections based on training data. This process often involves comparing the network's output to the desired output and using algorithms like backpropagation to minimize errors.

Essentially, artificial neural networks are **software programs or algorithms** that leverage computing power to perform these complex **mathematical calculations** rapidly and at scale, allowing them to learn patterns and make decisions or predictions based on data.

Here's a simplified look at the core components:

| Component          | Description                                     | Function in ANN                     |
| :----------------- | :---------------------------------------------- | :---------------------------------- |
| **Artificial Neuron** | Software module (Node)                          | Basic processing unit             |
| **Connection**     | Link between neurons with associated weight     | Transmits information and influence |
| **Layers**         | Groups of neurons (Input, Hidden, Output)       | Organize processing flow            |
| **Algorithm**      | Defines how neurons connect and process data    | Governs network behavior            |
| **Computing System** | Hardware that runs the software/algorithm       | Provides processing power           |

By combining these elements and training them on relevant data, developers can create ANNs capable of solving a wide range of complex problems that are difficult for traditional algorithms.