A neural network is a machine learning model inspired by the human brain. Here are some key things to know about neural networks:
Neural networks contain interconnected nodes called neurons that pass signals to each other. They are organized in interconnected layers – an input layer, multiple hidden layers, and an output layer.
Each neuron receives input signals and performs mathematical operations to determine an output value. The connections between neurons have numeric weights associated with them that are tuned during training.
Neural networks have the ability to learn complex patterns and relationships in large amounts of data through a process called training. During training, examples are fed into the network and adjusting weights allows the network to fit the data.
Common neural network models include multilayer perceptrons (MLP), convolutional neural networks (CNNs), recurrent neural networks (RNNs).
MLPs are feedforward networks used for classification and regression tasks. CNNs are used primarily for image and video recognition. RNNs work well for sequence data like text and time series.
Neural networks excel at complex pattern recognition tasks like image classification, speech recognition, machine translation etc. where traditional algorithms perform poorly.
Their ability to model highly complex nonlinear relationships is what makes them very powerful predictive models.
So in summary, neural networks mimic the human brain in learning from large amounts of data through adjustment of weights between interconnected nodes.
