Neural networks are a type of machine learning model inspired by the structure of the human brain. They are made up of layers of nodes that are often called neurons, and these layers work together to process and transform data. At AEHEA, we use neural networks when we need systems that can identify complex patterns, like recognizing faces in images, translating languages, or generating humanlike text. These networks are behind many of the AI advancements you see today.

Each node in a neural network receives input, performs a simple computation, and passes its output to the next layer. The network typically starts with an input layer, where the raw data enters, followed by one or more hidden layers where the real processing happens. These hidden layers apply weights to the data, adjust those weights based on feedback, and learn to make better predictions over time. Finally, the output layer produces a result, whether it is a classification, a score, or some generated content.

The learning process involves feeding the network many examples and adjusting the weights to reduce the error between the prediction and the correct answer. This is called training, and it often uses an algorithm called backpropagation, which compares the output to the expected result and moves backward through the network to improve accuracy. The more data and the more iterations, the better the network becomes at making accurate predictions. However, the quality of learning depends heavily on the structure of the network and the quality of the data.

At AEHEA, we view neural networks as flexible and powerful tools, but not always the right solution for every task. They work best when the data is abundant and complex, and when traditional rule-based systems fall short. Whether it is a simple feedforward network or a more advanced architecture like a transformer, these systems allow us to build AI that can adapt, learn, and scale across a wide range of applications. We use them carefully and strategically to ensure the right balance between performance, transparency, and efficiency.