In AI, there are many different types of neural networks with unique structures, but for this introduction, we will discuss a specific type: fully connected neural networks, which are often used in combination with other neural networks in recognizing images.
In fully connected neural networks, nodes are lined up into columns, which are called layers. As the name suggests, every node in every layer except for the last is connected to every node in the next layer. In this neural network, the number of layers and the number of nodes in each layer may vary; however, the property of being “fully connected” doesn’t change.
As usual, each node holds a number, and connections between nodes multiply that number by a certain amount and pass it onto the next node. In this case, if a node has multiple connections coming into it, all the data from each connection is added together. Usually, before data is stored by the next node, it’s also plugged into a function and thus further modified.
Then, where are the input and the output? Initially, all of the nodes are empty, and raw input data fills in the leftmost layer of the diagram. Then, connections modify and transfer these data to further nodes, and the data is gradually moved to the right as it’s processed. Finally, the right-most layer’s nodes get their data and output it. You might ask how is this relevant to decision making, but in fact, the output from the rightmost layer is often assigned significance. For example, each output node represents a possible decision, and the node with the largest number is the decision the neural network makes.
Of course, a neural network needs to know exactly what amount to multiply the data by in each connection to be functional: this process of obtaining the “settings” of the neural network is known as training. However, for this introductory article, you just have to know that neural networks are sophisticated structures of nodes and connections, and they process input data to make a decision.
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