Computer Science – 7.1 Ethics and Ownership | e-Consult

Neural Networks: Neural networks are computational models inspired by the structure and function of the human brain. They consist of interconnected nodes (neurons) organized in layers. Each connection between neurons has a weight associated with it, representing the strength of the connection. Information flows through the network, with each neuron performing a simple calculation and passing the result to the next layer.

Activation Functions: Activation functions introduce non-linearity into the network, allowing it to learn complex patterns. Common activation functions include sigmoid, ReLU, and tanh. They determine the output of a neuron based on the weighted sum of its inputs. Without activation functions, a neural network would essentially be a linear model, severely limiting its capabilities.

Backpropagation: Backpropagation is an algorithm used to train neural networks. It works by calculating the error between the network's output and the desired output, and then propagating this error back through the network to adjust the weights of the connections. This process iteratively refines the weights, minimizing the error and improving the network's accuracy. The chain rule of calculus is fundamental to backpropagation.

Example Application: Image Recognition: Convolutional Neural Networks (CNNs) are widely used for image recognition. They are particularly effective at identifying patterns in images, such as edges, shapes, and textures. CNNs are trained on large datasets of labeled images to learn to recognize different objects. This allows AI systems to perform tasks like facial recognition, object detection in self-driving cars, and medical image analysis.