Artificial neural networks have become the backbone of deep learning, spurring breakthroughs infields like computer vision, natural language processing, and more.
But how exactly do these brain-inspired networks work, and what are some of the common architectures used? This article will provide an accessible explanation of neural networks and their inner workings.
No advanced math or coding needed!
Neural networks are computing systems that are loosely modeled after the neural networks found in animal brains. The fundamental unit of a neural network is the artificial neuron, also called a node or unit. These nodes are arranged in layers and connect to nodes in other layers (see Figure 1).
Figure 1. Simplified diagram of a neural network with input, hidden, and output layers_
Each connection between nodes has an associated weight, which determines the strength of the connection. Input data is fed into the input layer, passes through the network transforming as it goes, and the predictions/classifications are output at the end.
The “learning” occurs during training, as the weights are continually adjusted to improve the model’s predictions. The key aspects that enable neural networks to perform complex tasks are:
Multiple hidden layers to extract increasingly abstract features and patterns
– Non-linear activations to enable modeling complex relationships between inputs and outputs
– Backpropagation algorithm to calculate error contribution of each node and update weights accordingly
By stacking multiple hidden layers, we get deep neural networks capable of learning very sophisticated patterns and relationships within data.