AiVibeArtificial Neural Network
An artificial neural network is a machine learning model built from layers of interconnected units, loosely inspired by biological neurons, in which each unit computes a weighted sum of its inputs and applies a non-linear activation function.
A network consists of an input layer, one or more hidden layers and an output layer. Each connection has a weight and each unit a bias, and non-linear activation functions such as ReLU, sigmoid or tanh allow the network to model complex relationships. Training uses backpropagation to compute how the loss changes with each weight, and an optimiser such as stochastic gradient descent or Adam updates the weights over many iterations.
Neural networks are used for classification, regression, forecasting, control and generation. Simple multilayer perceptrons handle tabular and sensor data, while specialised architectures such as convolutional and transformer networks handle images, sequences and text. They are the building blocks of deep learning and of large language models.
Neural networks can overfit, so practitioners use validation data, regularisation, dropout and early stopping. Choices such as architecture, learning rate and batch size, known as hyperparameters, strongly affect results, and inputs normally need scaling before training. Their decisions are hard to explain, which matters in regulated or safety-relevant uses.
Key points
- Layers of units compute weighted sums followed by non-linear activations
- Backpropagation computes gradients; optimisers such as Adam update weights
- The building block of deep learning and large language models
- Regularisation, dropout and validation data help prevent overfitting
Where AiVibe comes in
AiVibe delivers AI and machine learning services, chatbots and virtual assistants with RAG, MCP tools and voice, AI quality management including bias detection and model validation, and the AIMURUGA AI agent, and builds Intel-based edge AI devices using the Intel Distribution of OpenVINO toolkit.