A neural network refers to a computational model inspired by the structure and functioning of biological neural networks found in the human brain. It consists of interconnected nodes called neurons, organized in layers. Each neuron processes input data, applies weights and biases, and passes the result through an activation function to produce an output. Neural networks are designed to learn from data through iterative adjustments of weights, enabling them to recognize patterns, make predictions, or perform tasks such as classification and regression based on learned patterns.
An example of a neural network is a feedforward neural network used for image classification. In this scenario, the neural network comprises an input layer receiving pixel values of an image, hidden layers that perform computations and feature extraction through weighted connections, and an output layer that predicts the class label of the image (e.g., cat or dog). Through training with labeled images, the network learns to identify features like edges, textures, and shapes, enabling accurate classification of new, unseen images based on learned patterns.
A convolutional neural network (CNN) is a specialized type of neural network designed for processing structured grid-like data, such as images. CNNs incorporate convolutional layers that apply filters to input data, extracting spatial hierarchies and features like edges and textures. These layers are followed by pooling layers that downsample feature maps to reduce spatial dimensions while preserving essential information. CNNs are widely used in tasks such as image recognition, object detection, and image segmentation due to their ability to automatically learn and identify patterns in visual data with high accuracy and efficiency.
In deep learning, a neuron refers to a fundamental unit within a neural network that performs computations on input data. Each neuron receives input signals, applies weights and biases to these inputs, computes a weighted sum, and then passes the result through an activation function to produce an output. Neurons are organized in layers within neural networks, where each layer processes and transforms data representations to capture increasingly abstract features. Neurons in deep learning models, including CNNs, play a crucial role in learning and representing complex relationships and patterns in data, enabling the network to make predictions or decisions based on learned knowledge.