Neural networks (also known as Artificial Neural Networks or ANNs) are computational models inspired by the biological structure and functioning of human brains. They form the core foundation of modern Artificial Intelligence and Deep Learning.
Key Features
- Layered Architecture: Organized into an input layer, one or more hidden layers, and an output layer. Deep neural networks contain multiple hidden layers to extract high-level feature representations.
- Artificial Neurons (Nodes): The basic processing units that receive inputs, multiply them by adjustable weights, add a bias, and pass the result through an activation function.
- Activation Functions: Introduce non-linearity into the network, allowing it to learn and model complex, non-linear relationships. Common functions include ReLU, Sigmoid, and Tanh.
- Backpropagation: The fundamental learning algorithm that calculates the gradient of the loss function with respect to the network’s weights, propagating errors backward to update parameters.
- Optimization Algorithms: Methods like Gradient Descent and Adam that iteratively adjust weights and biases to minimize the loss/error.
- Feature Learning: Automatically extract and learn hierarchically structured features from raw inputs (e.g., edges to shapes to faces in image recognition), reducing the need for manual feature engineering.
Major Architectures
- Feedforward Neural Networks (FNN): The simplest design where connections do not form cycles; information moves strictly forward.
- Convolutional Neural Networks (CNN): Optimized for spatial data like images, utilizing convolutional layers to detect local patterns.
- Recurrent Neural Networks (RNN): Designed for sequential or time-series data, featuring connections that loop back to maintain a memory of past inputs.
- Transformers: Modern architectures leveraging self-attention mechanisms, dominating natural language processing and generative AI.