Neural Networks
Artificial neural networks are computing systems inspired by the biological neural networks that constitute animal brains. They are composed of interconnected nodes, or “neurons,” organized into layers that process inputs by applying mathematical transformations and activation functions to produce an output. Neural networks are highly capable of learning complex, non-linear relationships within vast amounts of data, making them the foundational architecture for modern deep learning models. Their versatility allows them to be applied across a wide range of tasks, from image and speech recognition to autonomous driving and natural language processing.
Key Concepts
- Perceptron and Neurons: The basic computational unit in a neural network that receives inputs, multiplies them by learned weights, adds a bias term, and passes the sum through an activation function.
- Layers: Neural networks are typically organized into an input layer, one or more hidden layers, and an output layer. Deep learning refers to networks with many hidden layers.
- Activation Functions: Mathematical equations (such as ReLU, Sigmoid, or Tanh) that introduce non-linearity into the network, allowing it to learn complex patterns beyond simple linear combinations.
- Backpropagation: The primary algorithm used to train neural networks, which calculates the gradient of the loss function with respect to the network’s weights by propagating the error backwards from the output layer to the input layer.
- Gradient Descent: An optimization algorithm used in conjunction with backpropagation to update the network’s weights iteratively, minimizing the overall loss.