What is a Neural Network?
A neural network is a computational model inspired by the way biological neural networks in the human brain process information. It is a key component of deep learning, which is a subset of machine learning and artificial intelligence. Neural networks consist of interconnected layers of nodes, each representing a neuron that processes input data and produces an output. These layers are typically categorized into three types: input, hidden, and output layers.
The input layer receives the initial data, while the hidden layers perform computations through learned weights and activation functions that introduce non-linearities. The output layer produces the final prediction or classification. Neural networks are particularly well-suited for tasks such as image and speech recognition, natural language processing, and more.
Training a neural network involves using algorithms like backpropagation, where the model adjusts its internal parameters based on error rates calculated against the expected output. The goal is to minimize the difference between the predicted and actual results, enhancing the model's accuracy over time.
Overall, neural networks represent a powerful tool for tackling complex problems in various fields, leveraging large datasets to learn patterns and make informed decisions.