What are Neural Networks? - Definition & Meaning

Definition

Neural networks are computational models inspired by the structure of the human brain. They consist of interconnected "neurons" that process information via weights and activation functions, enabling the system to learn complex patterns.

Technical explanation

A neural network contains input layers, hidden layers, and output layers. Each connection has a weight that is adjusted during training via backpropagation and gradient descent. Architectures range from simple feedforward networks to CNNs (for image processing), RNNs/LSTMs (for sequential data), and Transformers (for language and many other tasks). Deep learning refers to networks with many hidden layers. Modern LLMs and vision models are transformer-based neural networks with billions of parameters.

How AVARC Solutions applies this

AVARC Solutions uses neural networks as the foundation for our AI solutions. From recommendation systems and language models to image recognition and predictive models, we build and integrate neural networks that automate complex tasks and extract valuable insights from data.

Practical examples

• An LLM like GPT trained on vast text corpora that generates natural language thanks to transformer neural networks.
• A recommendation system combining user behavior and product features through a neural network for personalized suggestions.
• An image classification model using a CNN to recognize defects in product images for automatic quality control.

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